diff --git a/.github/workflows/black_checker.yml b/.github/workflows/black_checker.yml new file mode 100644 index 0000000000..fac1723682 --- /dev/null +++ b/.github/workflows/black_checker.yml @@ -0,0 +1,47 @@ +name: black-format-check + +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development + +env: + #If STRICT is set to true, it will fail on black check fail + STRICT: false + +jobs: + + black-format-check: + runs-on: ubuntu-latest + steps: + + - name: Checkout + uses: actions/checkout@v2 + with: + submodules: recursive + + - name: Setup Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: "3.7" + + - name: Install black + run: | + pip install black + + - name: Run Black Check + run: | + black --check --diff --line-length 100 ./autosklearn || ! $STRICT + black --check --diff --line-length 100 ./test || ! $STRICT + black --check --diff --line-length 100 ./examples|| ! $STRICT diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index ada0593183..29eb0850dc 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -1,31 +1,58 @@ name: dist-check -on: [push, pull_request] +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development jobs: dist: runs-on: ubuntu-latest + steps: - - uses: actions/checkout@v2 + - name: Check out the repo + uses: actions/checkout@v2 + with: + submodules: recursive + - name: Setup Python uses: actions/setup-python@v2 with: python-version: 3.8 + - name: Build dist run: | python setup.py sdist + - name: Twine check run: | pip install twine last_dist=$(ls -t dist/auto-sklearn-*.tar.gz | head -n 1) twine_output=`twine check "$last_dist"` if [[ "$twine_output" != "Checking $last_dist: PASSED" ]]; then echo $twine_output && exit 1;fi + - name: Install dist run: | last_dist=$(ls -t dist/auto-sklearn-*.tar.gz | head -n 1) pip install $last_dist + - name: PEP 561 Compliance run: | pip install mypy + cd .. # required to use the installed version of autosklearn - if ! python -c "import autosklearn"; then exit 1; fi + + # Note this doesnt perform mypy checks, only + # that the types are exported + if ! mypy -c "import autosklearn"; then exit 1; fi diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index fe8c7f154e..3a9af5bf94 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -1,24 +1,31 @@ #https://help.github.com/en/actions/language-and-framework-guides/publishing-docker-images#publishing-images-to-github-packages name: Publish Docker image + on: + push: - # Push to `master` or `development` branches: - master - development - docker_workflow jobs: + push_to_registry: name: Push Docker image to GitHub Packages runs-on: ubuntu-latest + steps: - name: Check out the repo uses: actions/checkout@v2 + with: + submodules: recursive + - name: Extract branch name shell: bash run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})" id: extract_branch + - name: Push to GitHub Packages uses: docker/build-push-action@v1 with: @@ -28,6 +35,7 @@ jobs: repository: automl/auto-sklearn/auto-sklearn tag_with_ref: true tags: ${{ steps.extract_branch.outputs.branch }} + - name: Push to Docker Hub uses: docker/build-push-action@v1 with: @@ -35,19 +43,24 @@ jobs: password: ${{ secrets.DOCKER_PASSWORD }} repository: mfeurer/auto-sklearn tags: ${{ steps.extract_branch.outputs.branch }} + - name: Docker Login run: docker login docker.pkg.github.com -u $GITHUB_ACTOR -p $GITHUB_TOKEN env: GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + - name: Pull Docker image run: docker pull docker.pkg.github.com/$GITHUB_REPOSITORY/auto-sklearn:$BRANCH env: BRANCH: ${{ steps.extract_branch.outputs.branch }} + - name: Run image run: docker run -i -d --name unittester -v $GITHUB_WORKSPACE:/workspace -w /workspace docker.pkg.github.com/$GITHUB_REPOSITORY/auto-sklearn:$BRANCH env: BRANCH: ${{ steps.extract_branch.outputs.branch }} + - name: Auto-Sklearn loaded run: docker exec -i unittester python3 -c 'import autosklearn; print(f"Auto-sklearn imported from {autosklearn.__file__}")' + - name: Run unit testing run: docker exec -i unittester python3 -m pytest -v test diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 12c1f9f390..3645596c7b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -1,31 +1,57 @@ name: Docs -on: [pull_request, push] + +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development jobs: + build-and-deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + + - name: Checkout + uses: actions/checkout@v2 + with: + submodules: recursive + - name: Setup Python uses: actions/setup-python@v2 with: python-version: 3.8 + - name: Install dependencies run: | - pip install -e .[docs,examples,examples_unix] + pip install -e .[docs,examples] + - name: Make docs run: | cd doc make html + - name: Check links run: | cd doc make linkcheck + - name: Pull latest gh-pages if: (contains(github.ref, 'develop') || contains(github.ref, 'master')) && github.event_name == 'push' run: | cd .. git clone https://github.com/automl/auto-sklearn.git --branch gh-pages --single-branch gh-pages + - name: Copy new doc into gh-pages if: (contains(github.ref, 'develop') || contains(github.ref, 'master')) && github.event_name == 'push' run: | @@ -33,6 +59,7 @@ jobs: cd ../gh-pages rm -rf $branch_name cp -r ../auto-sklearn/doc/build/html $branch_name + - name: Push to gh-pages if: (contains(github.ref, 'develop') || contains(github.ref, 'master')) && github.event_name == 'push' run: | diff --git a/.github/workflows/isort_checker.yml b/.github/workflows/isort_checker.yml new file mode 100644 index 0000000000..4f1f03f5a8 --- /dev/null +++ b/.github/workflows/isort_checker.yml @@ -0,0 +1,45 @@ +name: isort-check + +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development + +env: + #If STRICT is set to true, it will fail on isort check fail + STRICT: false + +jobs: + + isort-format-check: + runs-on: ubuntu-latest + steps: + + - name: Checkout + uses: actions/checkout@v2 + with: + submodules: recursive + + - name: Setup Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: "3.7" + + - name: Install isort + run: | + pip install isort + + - name: Run isort Check + run: | + isort --check-only autosklearn || ! $STRICT diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml index eabada7e8d..03ca861dff 100644 --- a/.github/workflows/pre-commit.yaml +++ b/.github/workflows/pre-commit.yaml @@ -1,20 +1,39 @@ name: pre-commit -on: [push, pull_request] +on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches + push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches + pull_request: + branches: + - master + - development jobs: run-all-files: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 + with: + submodules: recursive + - name: Setup Python 3.7 uses: actions/setup-python@v2 with: python-version: 3.7 + - name: Install pre-commit run: | pip install pre-commit pre-commit install + - name: Run pre-commit run: | pre-commit run --all-files diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 513d8ff07f..4a9feba75f 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -1,102 +1,138 @@ name: Tests on: + # Manually triggerable in github + workflow_dispatch: + + # When a push occurs on either of these branches push: + branches: + - master + - development + + # When a push occurs on a PR that targets these branches pull_request: + branches: + - master + - development + schedule: - # Every Monday at 7AM UTC - - cron: '0 07 * * 1' + # Every day at 7AM UTC + - cron: '0 07 * * *' + +env: + + # Arguments used for pytest + pytest-args: >- + --forked + --durations=20 + --timeout=300 + --timeout-method=thread + -s + + # Arguments used for code-cov which is later used to annotate PR's on github + code-cov-args: >- + --cov=autosklearn + --cov-report=xml jobs: + ubuntu: - runs-on: ubuntu-20.04 + + name: ${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.kind }} + runs-on: ${{ matrix.os }} strategy: + fail-fast: false matrix: - python-version: [3.7, 3.8, 3.9] - use-conda: [true, false] - use-dist: [false] + os: [windows-latest, macos-latest, ubuntu-latest] + python-version: ['3.7', '3.8', '3.9', '3.10'] + kind: ['conda', 'source', 'dist'] + + exclude: + # Exclude all configurations *-*-dist, include one later + - kind: 'dist' + + # Exclude windows as bash commands wont work in windows runner + - os: windows-latest + + # Exclude macos as there are permission errors using conda as we do + - os: macos-latest + include: - - python-version: 3.8 + # Add the tag code-cov to ubuntu-3.7-source + - os: ubuntu-latest + python-version: 3.7 + kind: 'source' code-cov: true - - python-version: 3.7 - use-conda: false - use-dist: true - fail-fast: false + + # Include one config with dist, ubuntu-3.7-dist + - os: ubuntu-latest + python-version: 3.7 + kind: 'dist' steps: - - uses: actions/checkout@v2 + - name: Checkout + uses: actions/checkout@v2 + with: + submodules: recursive + - name: Setup Python ${{ matrix.python-version }} uses: actions/setup-python@v2 - # A note on checkout: When checking out the repository that - # triggered a workflow, this defaults to the reference or SHA for that event. - # Otherwise, uses the default branch (master) is used. with: python-version: ${{ matrix.python-version }} - - name: Conda Install test dependencies - if: matrix.use-conda == true + - name: Conda install + if: matrix.kind == 'conda' run: | # Miniconda is available in $CONDA env var $CONDA/bin/conda create -n testenv --yes pip wheel gxx_linux-64 gcc_linux-64 swig python=${{ matrix.python-version }} $CONDA/envs/testenv/bin/python3 -m pip install --upgrade pip $CONDA/envs/testenv/bin/pip3 install -e .[test] - - name: Install test dependencies - if: matrix.use-conda == false && matrix.use-dist == false + - name: Source install + if: matrix.kind == 'source' run: | python -m pip install --upgrade pip - if [[ `python -c 'import platform; print(platform.python_version())' | cut -d '.' -f 2` -eq 6 ]]; then - # Numpy 1.20 dropped suppert for Python3.6 - pip install "numpy<=1.19" - fi pip install -e .[test] - sudo apt-get update - sudo apt-get remove swig - sudo apt-get install swig3.0 - sudo ln -s /usr/bin/swig3.0 /usr/bin/swig - - name: Dist Install test dependencies - if: matrix.use-conda == false && matrix.use-dist == true + - name: Dist install + if: matrix.kind == 'dist' run: | python -m pip install --upgrade pip - sudo apt-get update - sudo apt-get remove swig - sudo apt-get install swig3.0 - sudo ln -s /usr/bin/swig3.0 /usr/bin/swig - # We need to install for the dependencies, like pytest python setup.py sdist last_dist=$(ls -t dist/auto-sklearn-*.tar.gz | head -n 1) pip install $last_dist[test] - - name: Store repository status + - name: Store git status id: status-before run: | echo "::set-output name=BEFORE::$(git status --porcelain -b)" - - name: Conda Run tests + - name: Tests timeout-minutes: 60 - if: matrix.use-conda == true run: | export OPENBLAS_NUM_THREADS=1 export OMP_NUM_THREADS=1 export MKL_NUM_THREADS=1 - # We activate conda as metalearning uses python directly, so we need - # to change the default python - export PATH="$CONDA/envs/testenv/bin:$PATH" - if [ ${{ matrix.code-cov }} ]; then codecov='--cov=autosklearn --cov-report=xml'; fi - $CONDA/envs/testenv/bin/python3 -m pytest --durations=20 --timeout=300 --timeout-method=thread -v $codecov test - - name: Run tests - timeout-minutes: 60 - if: matrix.use-conda == false - run: | - export OPENBLAS_NUM_THREADS=1 - export OMP_NUM_THREADS=1 - export MKL_NUM_THREADS=1 - if [ ${{ matrix.code-cov }} ]; then codecov='--cov=autosklearn --cov-report=xml'; fi - pytest --durations=20 --timeout=300 --timeout-method=thread -v $codecov test + if [[ ${{ matrix.kind }} == 'conda' ]]; then + PYTHON=$CONDA/envs/testenv/bin/python3 + + # As one of the tests runs a subprocess command and calls `python3`, we must + # explicitly add it to the path + export PATH="$CONDA/envs/testenv/bin:$PATH" + + else + PYTHON=$(which python3) + fi + + if [ ${{ matrix.code-cov }} ]; then + $PYTHON -m pytest ${{ env.pytest-args }} ${{ env.code-cov-args }} test + else + $PYTHON -m pytest ${{ env.pytest-args }} test + fi - name: Check for files left behind by test if: ${{ always() }} @@ -112,7 +148,7 @@ jobs: - name: Upload coverage if: matrix.code-cov && always() - uses: codecov/codecov-action@v1 + uses: codecov/codecov-action@v2 with: fail_ci_if_error: true verbose: true diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index 45422d04eb..d95d344674 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -1,9 +1,11 @@ name: 'Close stale issues' + on: schedule: - - cron: '30 1 * * *' + - cron: '0 7 * * *' jobs: + stale: runs-on: ubuntu-latest steps: @@ -11,12 +13,14 @@ jobs: with: days-before-stale: 60 days-before-close: 7 + stale-issue-label: 'stale' + only-issue-labels: 'Answered,Feedback-Required,invalid,wontfix' + exempt-all-milestones: true + stale-issue-message: > This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs for the next 7 days. Thank you for your contributions. + close-issue-message: > This issue has been automatically closed due to inactivity. - stale-issue-label: 'stale' - only-issue-labels: 'Answered,Feedback-Required,invalid,wontfix' - exempt-all-milestones: true diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000..28a5492b66 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "autosklearn/automl_common"] + path = autosklearn/automl_common + url = https://github.com/automl/automl_common diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 38f0280a32..a067f4b155 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -30,10 +30,12 @@ Following that we'll tell you about how you can test your changes locally and th It's important to work off the latest changes on the **development** branch. ```bash # With https - git clone https://github.com/your-username/auto-sklearn + # Note the --recurse-submodules args, we use a submodule autosklearn/automl_common + # so it needs to be downloaded too + git clone --recurse-submodules https://github.com/your-username/auto-sklearn # ... or with ssh - git clone git@github.com:your-username/auto-sklearn.git + git clone --recurse-submodules git@github.com:your-username/auto-sklearn.git # Navigate into the cloned repo cd auto-sklearn @@ -41,6 +43,11 @@ Following that we'll tell you about how you can test your changes locally and th # Create a new branch based off the development one git checkout -b my_new_branch development + # If you missed the --recurse-submodules arg during clone or need to install the + # submodule manually, then execute the following line: + # + # git submodule update --init --recursive + # ... Alternatively, if you would prefer a more manual method # Show all the available branches with a * beside your current one git branch @@ -50,6 +57,11 @@ Following that we'll tell you about how you can test your changes locally and th # Create a new branch based on the currently active branch git checkout -b my_new_branch + + # If you missed the --recurse-submodules arg during clone or need to install the + # submodule manually, then execute the following line: + # + # git submodule udate --init --recursive ``` The reason to create a new branch is two fold: @@ -81,7 +93,7 @@ Following that we'll tell you about how you can test your changes locally and th # If you're using shells other than bash you'll need to use pip install -e ".[test,examples,doc]" ``` - * If you're only exposure to using pip is `pip install package_name` then this might be a bit confusing. + * If your only exposure to using pip is `pip install package_name` then this might be a bit confusing. * If we type `pip install -e .` (notice the 'dot'), this tells `pip` to install a package located here, in this directory, `.`. The `-e` flag indicates that it should be editable, meaning you will not have to run `pip install .` every time you make a change and want to try it. * Finally the `[test,examples,doc]` tells `pip` that there's some extra optional dependencies that we want to install. @@ -335,6 +347,9 @@ Lastly, if the feature really is a game changer or you're very proud of it, cons cd auto-sklearn git checkout -b my_new_branch development + # Initialize autosklearn/automl_common submodule + git submodule update --init --recursive + # Create a virtual environment and activate it so there are no package # conflicts python -m venv my-virtual-env diff --git a/Dockerfile b/Dockerfile index d9f73b2c83..e2a74c04f6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -32,7 +32,7 @@ ADD . /auto-sklearn/ # Upgrade pip then install dependencies RUN pip3 install --upgrade pip -RUN pip3 install pytest==4.6.* pep8 codecov pytest-cov flake8 flaky openml +RUN pip3 install pytest==4.6.* pep8 codecov pytest-cov flake8 openml RUN cat /auto-sklearn/requirements.txt | xargs -n 1 -L 1 pip3 install RUN pip3 install jupyter diff --git a/MANIFEST.in b/MANIFEST.in index e76cdbb0ea..dffd0c7283 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,9 +1,18 @@ -recursive-include autosklearn/metalearning/files *.arff -recursive-include autosklearn/metalearning/files *.csv -recursive-include autosklearn/metalearning/files *.txt -include autosklearn/util/logging.yaml +include LICENSE.txt include requirements.txt +include autosklearn/util/logging.yaml include autosklearn/requirements.txt -recursive-include autosklearn/experimental/ *.json -include autosklearn/experimental/askl2_training_data.json -include LICENSE.txt +include autosklearn/py.typed + +# Meta-data +recursive-include autosklearn/metalearning/files *.arff *.csv *.txt +recursive-include autosklearn/experimental *.json + +# Remove tests from automl_common +prune autosklearn/automl_common/test +exclude autosklearn/automl_common/setup.py + +# Include automl_common LICENSE and README +include autosklearn/automl_common/LICENSE +include autosklearn/automl_common/README.md + diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index dae47a1089..f4769335d2 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -20,8 +20,8 @@ sys.platform ) -if sys.version_info < (3, 6): +if sys.version_info < (3, 7): raise ValueError( 'Unsupported python version %s found. Auto-sklearn requires Python ' - '3.6 or higher.' % sys.version_info + '3.7 or higher.' % sys.version_info ) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index d33bd90441..f524395e3a 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.14.3" +__version__ = "0.14.4" diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 064a887a4a..76640a5cbe 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,5 +1,6 @@ # -*- encoding: utf-8 -*- import copy +import distro import io import json import platform @@ -37,6 +38,8 @@ from sklearn.metrics._classification import type_of_target from sklearn.dummy import DummyClassifier, DummyRegressor +from autosklearn.automl_common.common.utils.backend import Backend, create + from autosklearn.metrics import Scorer, default_metric_for_task from autosklearn.data.xy_data_manager import XYDataManager from autosklearn.data.validation import ( @@ -49,7 +52,6 @@ from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings from autosklearn.evaluation.train_evaluator import TrainEvaluator, _fit_with_budget from autosklearn.metrics import calculate_metric -from autosklearn.util.backend import Backend, create from autosklearn.util.stopwatch import StopWatch from autosklearn.util.logging_ import ( setup_logger, @@ -171,8 +173,8 @@ def __init__(self, memory_limit=3072, metadata_directory=None, debug_mode=False, - include=None, - exclude=None, + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, resampling_strategy='holdout-iterative-fit', resampling_strategy_arguments=None, n_jobs=None, @@ -280,6 +282,8 @@ def __init__(self, def _create_backend(self) -> Backend: return create( temporary_directory=self._temporary_directory, + output_directory=None, + prefix="auto-sklearn", delete_tmp_folder_after_terminate=self._delete_tmp_folder_after_terminate, ) @@ -690,11 +694,10 @@ def fit( self._logger.debug('Starting to print environment information') self._logger.debug(' Python version: %s', sys.version.split('\n')) try: - self._logger.debug(' Distribution: %s', platform.linux_distribution()) + self._logger.debug(f'\tDistribution: {distro.id()}-{distro.version()}-{distro.name()}') except AttributeError: - # platform.linux_distribution() was removed in Python3.8 - # We should move to the distro package as soon as it supports Windows and OSX pass + self._logger.debug(' System: %s', platform.system()) self._logger.debug(' Machine: %s', platform.machine()) self._logger.debug(' Platform: %s', platform.platform()) @@ -1833,21 +1836,159 @@ def get_models_with_weights(self): return self.ensemble_.get_models_with_weights(self.models_) - def show_models(self): - models_with_weights = self.get_models_with_weights() + def show_models(self) -> Dict[int, Any]: + """ Returns a dictionary containing dictionaries of ensemble models. + + Each model in the ensemble can be accessed by giving its ``model_id`` as key. + + A model dictionary contains the following: + + * ``"model_id"`` - The id given to a model by ``autosklearn``. + * ``"rank"`` - The rank of the model based on it's ``"cost"``. + * ``"cost"`` - The loss of the model on the validation set. + * ``"ensemble_weight"`` - The weight given to the model in the ensemble. + * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling). + * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` (for 'cv' resampling). + * ``"data_preprocessor"`` - The preprocessor used on the data. + * ``"balancing"`` - The balancing used on the data (for classification). + * ``"feature_preprocessor"`` - The preprocessor for features types. + * ``"classifier"`` or ``"regressor"`` - The autosklearn wrapped classifier or regressor. + * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` - The sklearn classifier or regressor. + + **Example** + + .. code-block:: python + + import sklearn.datasets + import sklearn.metrics + import autosklearn.regression + + X, y = sklearn.datasets.load_diabetes(return_X_y=True) + + automl = autosklearn.regression.AutoSklearnRegressor( + time_left_for_this_task=120 + ) + automl.fit(X_train, y_train, dataset_name='diabetes') + + ensemble_dict = automl.show_models() + print(ensemble_dict) + + Output: + + .. code-block:: text + + { + 25: {'model_id': 25.0, + 'rank': 1, + 'cost': 0.43667876507897496, + 'ensemble_weight': 0.38, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...) + }, + 6: {'model_id': 6.0, + 'rank': 2, + 'cost': 0.4550418898836528, + 'ensemble_weight': 0.3, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...) + }... + } + + Returns + ------- + Dict(int, Any) : dictionary of length = number of models in the ensemble + A dictionary of models in the ensemble, where ``model_id`` is the key. - with io.StringIO() as sio: - sio.write("[") - for weight, model in models_with_weights: - sio.write("(%f, %s),\n" % (weight, model)) - sio.write("]") + """ - return sio.getvalue() + ensemble_dict = {} + + def has_key(rv, key): + return rv.additional_info and key in rv.additional_info + + table_dict = {} + for rkey, rval in self.runhistory_.data.items(): + if has_key(rval, 'num_run'): + model_id = rval.additional_info['num_run'] + table_dict[model_id] = { + 'model_id': model_id, + 'cost': rval.cost + } + + # Checking if the dictionary is empty + if not table_dict: + raise RuntimeError('No model found. Try increasing \'time_left_for_this_task\'.') + + for i, weight in enumerate(self.ensemble_.weights_): + (_, model_id, _) = self.ensemble_.identifiers_[i] + table_dict[model_id]['ensemble_weight'] = weight + + table = pd.DataFrame.from_dict(table_dict, orient='index') + + # Checking which resampling strategy is chosen and selecting the appropriate models + is_cv = (self._resampling_strategy == "cv") + models = self.cv_models_ if is_cv else self.models_ + + rank = 1 # Initializing rank for the first model + for (_, model_id, _), model in models.items(): + model_dict = {} # Declaring model dictionary + + # Inserting model_id, rank, cost and ensemble weight + model_dict['model_id'] = table.loc[model_id]['model_id'].astype(int) + model_dict['rank'] = rank + model_dict['cost'] = table.loc[model_id]['cost'] + model_dict['ensemble_weight'] = table.loc[model_id]['ensemble_weight'] + rank += 1 # Incrementing rank by 1 for the next model + + # The steps in the models pipeline are as follows: + # 'data_preprocessor': DataPreprocessor, + # 'balancing': Balancing, + # 'feature_preprocessor': FeaturePreprocessorChoice, + # 'classifier'/'regressor': ClassifierChoice/RegressorChoice (autosklearn wrapped model) + + # For 'cv' (cross validation) strategy + if is_cv: + # Voting model created by cross validation + cv_voting_ensemble = model + model_dict['voting_model'] = cv_voting_ensemble + + # List of models, each trained on one cv fold + cv_models = [] + for cv_model in cv_voting_ensemble.estimators_: + estimator = dict(cv_model.steps) + + # Adding sklearn model to the model dictionary + model_type, autosklearn_wrapped_model = cv_model.steps[-1] + estimator[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + cv_models.append(estimator) + model_dict['estimators'] = cv_models + + # For any other strategy + else: + steps = dict(model.steps) + model_dict.update(steps) - def _create_search_space(self, tmp_dir, backend, datamanager, - include=None, - exclude=None, - ): + # Adding sklearn model to the model dictionary + model_type, autosklearn_wrapped_model = model.steps[-1] + model_dict[f'sklearn_{model_type}'] = autosklearn_wrapped_model.choice.estimator + + # Insterting model_dict in the ensemble dictionary + ensemble_dict[model_id] = model_dict + + return ensemble_dict + + def _create_search_space( + self, + tmp_dir, + backend, + datamanager, + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, + ): task_name = 'CreateConfigSpace' self._stopwatch.start_task(task_name) diff --git a/autosklearn/automl_common b/autosklearn/automl_common new file mode 160000 index 0000000000..4c8ab915e0 --- /dev/null +++ b/autosklearn/automl_common @@ -0,0 +1 @@ +Subproject commit 4c8ab915e007745611b9b7266137497839aba701 diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py index 53539dd293..e337726b0e 100644 --- a/autosklearn/ensemble_builder.py +++ b/autosklearn/ensemble_builder.py @@ -24,11 +24,12 @@ from smac.runhistory.runhistory import RunInfo, RunValue from smac.tae.base import StatusType -from autosklearn.util.backend import Backend +from autosklearn.automl_common.common.utils.backend import Backend +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble + from autosklearn.constants import BINARY_CLASSIFICATION from autosklearn.metrics import calculate_score, calculate_loss, Scorer from autosklearn.ensembles.ensemble_selection import EnsembleSelection -from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.util.logging_ import get_named_client_logger from autosklearn.util.parallel import preload_modules diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py index 31a69ae904..e10eee978f 100644 --- a/autosklearn/ensembles/singlebest_ensemble.py +++ b/autosklearn/ensembles/singlebest_ensemble.py @@ -5,10 +5,10 @@ from smac.runhistory.runhistory import RunHistory +from autosklearn.automl_common.common.utils.backend import Backend from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble from autosklearn.metrics import Scorer from autosklearn.pipeline.base import BasePipeline -from autosklearn.util.backend import Backend class SingleBest(AbstractEnsemble): diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 87aa2be317..3eb2d7b8c5 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -34,8 +34,8 @@ def __init__( max_models_on_disc=50, seed=1, memory_limit=3072, - include=None, - exclude=None, + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, resampling_strategy='holdout', resampling_strategy_arguments=None, tmp_folder=None, @@ -76,7 +76,7 @@ def __init__( ensemble_size : int, optional (default=50) Number of models added to the ensemble built by *Ensemble selection from libraries of models*. Models are drawn with - replacement. + replacement. If set to ``0`` no ensemble is fit. ensemble_nbest : int, optional (default=50) Only consider the ``ensemble_nbest`` models when building an @@ -96,21 +96,64 @@ def __init__( memory_limit : int, optional (3072) Memory limit in MB for the machine learning algorithm. `auto-sklearn` will stop fitting the machine learning algorithm if - it tries to allocate more than `memory_limit` MB. - If None is provided, no memory limit is set. - In case of multi-processing, `memory_limit` will be per job. - This memory limit also applies to the ensemble creation process. - - include : dict, optional (None) - If None, all possible algorithms are used. Otherwise specifies - set of algorithms for each added component is used. Include and - exclude are incompatible if used together on the same component - - exclude : dict, optional (None) - If None, all possible algorithms are used. Otherwise specifies - set of algorithms for each added component is not used. - Incompatible with include. Include and exclude are incompatible - if used together on the same component + it tries to allocate more than ``memory_limit`` MB. + + **Important notes:** + + * If ``None`` is provided, no memory limit is set. + * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is + ``n_jobs x memory_limit``. + * The memory limit also applies to the ensemble creation process. + + include : Optional[Dict[str, List[str]]] = None + If None, all possible algorithms are used. + + Otherwise, specifies a step and the components that are included in search. + See ``/pipeline/components//*`` for available components. + + Incompatible with parameter ``exclude``. + + **Possible Steps**: + + * ``"data_preprocessor"`` + * ``"balancing"`` + * ``"feature_preprocessor"`` + * ``"classifier"`` - Only for when when using ``AutoSklearnClasssifier`` + * ``"regressor"`` - Only for when when using ``AutoSklearnRegressor`` + + **Example**: + + .. code-block:: python + + include = { + 'classifier': ["random_forest"], + 'feature_preprocessor': ["no_preprocessing"] + } + + exclude : Optional[Dict[str, List[str]]] = None + If None, all possible algorithms are used. + + Otherwise, specifies a step and the components that are excluded from search. + See ``/pipeline/components//*`` for available components. + + Incompatible with parameter ``include``. + + **Possible Steps**: + + * ``"data_preprocessor"`` + * ``"balancing"`` + * ``"feature_preprocessor"`` + * ``"classifier"`` - Only for when when using ``AutoSklearnClasssifier`` + * ``"regressor"`` - Only for when when using ``AutoSklearnRegressor`` + + **Example**: + + .. code-block:: python + + exclude = { + 'classifier': ["random_forest"], + 'feature_preprocessor': ["no_preprocessing"] + } resampling_strategy : string or object, optional ('holdout') how to to handle overfitting, might need 'resampling_strategy_arguments' @@ -145,10 +188,10 @@ def __init__( * 'cv-iterative-fit': {'folds': int} * 'partial-cv': {'folds': int, 'shuffle': bool} * BaseCrossValidator or _RepeatedSplits or BaseShuffleSplit object: all arguments - required by chosen class as specified in scikit-learn documentation. - If arguments are not provided, scikit-learn defaults are used. - If no defaults are available, an exception is raised. - Refer to the 'n_splits' argument as 'folds'. + required by chosen class as specified in scikit-learn documentation. + If arguments are not provided, scikit-learn defaults are used. + If no defaults are available, an exception is raised. + Refer to the 'n_splits' argument as 'folds'. tmp_folder : string, optional (None) folder to store configuration output and log files, if ``None`` @@ -160,13 +203,15 @@ def __init__( n_jobs : int, optional, experimental The number of jobs to run in parallel for ``fit()``. ``-1`` means - using all processors. By default, Auto-sklearn uses a single core - for fitting the machine learning model and a single core for fitting - an ensemble. Ensemble building is not affected by ``n_jobs`` but - can be controlled by the number of models in the ensemble. In - contrast to most scikit-learn models, ``n_jobs`` given in the - constructor is not applied to the ``predict()`` method. If - ``dask_client`` is None, a new dask client is created. + using all processors. + + **Important notes**: + + * By default, Auto-sklearn uses one core. + * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number + of models in the ensemble. + * ``predict()`` is not affected by ``n_jobs`` (in contrast to most scikit-learn models) + * If ``dask_client`` is ``None``, a new dask client is created. dask_client : dask.distributed.Client, optional User-created dask client, can be used to start a dask cluster and then @@ -182,7 +227,7 @@ def __init__( * ``'y_optimization'`` : do not save the predictions for the optimization/validation set, which would later on be used to build an ensemble. - * ``'model'`` : do not save any model files + * ``model`` : do not save any model files smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the @@ -228,13 +273,13 @@ def __init__( Attributes ---------- - cv_results\_ : dict of numpy (masked) ndarrays + cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. Not all keys returned by scikit-learn are supported yet. - performance_over_time\_ : pandas.core.frame.DataFrame + performance_over_time_ : pandas.core.frame.DataFrame A ``DataFrame`` containing the models performance over time data. Can be used for plotting directly. Please refer to the example :ref:`Train and Test Inputs `. @@ -492,13 +537,74 @@ def score(self, X, y): return self.automl_.score(X, y) def show_models(self): - """Return a representation of the final ensemble found by auto-sklearn. + """ Returns a dictionary containing dictionaries of ensemble models. + + Each model in the ensemble can be accessed by giving its ``model_id`` as key. + + A model dictionary contains the following: + + * ``"model_id"`` - The id given to a model by ``autosklearn``. + * ``"rank"`` - The rank of the model based on it's ``"cost"``. + * ``"cost"`` - The loss of the model on the validation set. + * ``"ensemble_weight"`` - The weight given to the model in the ensemble. + * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling). + * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble`` (for 'cv' resampling). + * ``"data_preprocessor"`` - The preprocessor used on the data. + * ``"balancing"`` - The balancing used on the data (for classification). + * ``"feature_preprocessor"`` - The preprocessor for features types. + * ``"classifier"`` or ``"regressor"`` - The autosklearn wrapped classifier or regressor. + * ``"sklearn_classifier"`` or ``"sklearn_regressor"`` - The sklearn classifier or regressor. + + **Example** + + .. code-block:: python + + import sklearn.datasets + import sklearn.metrics + import autosklearn.regression + + X, y = sklearn.datasets.load_diabetes(return_X_y=True) + + automl = autosklearn.regression.AutoSklearnRegressor( + time_left_for_this_task=120 + ) + automl.fit(X_train, y_train, dataset_name='diabetes') + + ensemble_dict = automl.show_models() + print(ensemble_dict) + + Output: + + .. code-block:: text + + { + 25: {'model_id': 25.0, + 'rank': 1, + 'cost': 0.43667876507897496, + 'ensemble_weight': 0.38, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...) + }, + 6: {'model_id': 6.0, + 'rank': 2, + 'cost': 0.4550418898836528, + 'ensemble_weight': 0.3, + 'data_preprocessor': , + 'feature_preprocessor': , + 'regressor': , + 'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...) + }... + } Returns ------- - str + Dict(int, Any) : dictionary of length = number of models in the ensemble + A dictionary of models in the ensemble, where ``model_id`` is the key. """ + return self.automl_.show_models() def get_models_with_weights(self): @@ -559,7 +665,7 @@ def leaderboard( Gives an overview of all models trained during the search process along with various statistics about their training. - The availble statistics are: + The available statistics are: **Simple**: diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 589535d085..506cf51441 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -19,14 +19,15 @@ from sklearn.model_selection._split import _RepeatedSplits, BaseShuffleSplit,\ BaseCrossValidator -from autosklearn.metrics import Scorer +from autosklearn.automl_common.common.utils.backend import Backend + +from autosklearn.metrics import Scorer import autosklearn.evaluation.train_evaluator import autosklearn.evaluation.test_evaluator import autosklearn.evaluation.util import autosklearn.pipeline.components from autosklearn.evaluation.train_evaluator import TYPE_ADDITIONAL_INFO -from autosklearn.util.backend import Backend from autosklearn.util.logging_ import PickableLoggerAdapter, get_named_client_logger from autosklearn.util.parallel import preload_modules diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 2e398b00ae..36d51d7e0d 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -14,6 +14,8 @@ from threadpoolctl import threadpool_limits +from autosklearn.automl_common.common.utils.backend import Backend + import autosklearn.pipeline.classification import autosklearn.pipeline.regression from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons @@ -28,7 +30,6 @@ convert_multioutput_multiclass_to_multilabel ) from autosklearn.metrics import calculate_loss, Scorer -from autosklearn.util.backend import Backend from autosklearn.util.logging_ import PicklableClientLogger, get_named_client_logger from ConfigSpace import Configuration diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index e83edb0682..181ebce233 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -8,13 +8,14 @@ from smac.tae import StatusType +from autosklearn.automl_common.common.utils.backend import Backend + from autosklearn.evaluation.abstract_evaluator import ( AbstractEvaluator, _fit_and_suppress_warnings, ) from autosklearn.pipeline.components.base import ThirdPartyComponents from autosklearn.metrics import calculate_loss, Scorer -from autosklearn.util.backend import Backend __all__ = [ diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index 51b433153d..558fdd3b67 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -17,6 +17,8 @@ StratifiedKFold, train_test_split, BaseCrossValidator, PredefinedSplit from sklearn.model_selection._split import _RepeatedSplits, BaseShuffleSplit +from autosklearn.automl_common.common.utils.backend import Backend + from autosklearn.evaluation.abstract_evaluator import ( AbstractEvaluator, TYPE_ADDITIONAL_INFO, @@ -37,7 +39,6 @@ from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import IterativeComponent, ThirdPartyComponents from autosklearn.metrics import Scorer -from autosklearn.util.backend import Backend from autosklearn.util.logging_ import PicklableClientLogger diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 7cbeebc9d0..c01282fc47 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -218,7 +218,7 @@ def __init__( ensemble_size : int, optional (default=50) Number of models added to the ensemble built by *Ensemble selection from libraries of models*. Models are drawn with - replacement. + replacement. If set to ``0`` no ensemble is fit. ensemble_nbest : int, optional (default=50) Only consider the ``ensemble_nbest`` models when building an @@ -238,10 +238,14 @@ def __init__( memory_limit : int, optional (3072) Memory limit in MB for the machine learning algorithm. `auto-sklearn` will stop fitting the machine learning algorithm if - it tries to allocate more than `memory_limit` MB. - If None is provided, no memory limit is set. - In case of multi-processing, `memory_limit` will be per job. - This memory limit also applies to the ensemble creation process. + it tries to allocate more than ``memory_limit`` MB. + + **Important notes:** + + * If ``None`` is provided, no memory limit is set. + * In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is + ``n_jobs x memory_limit``. + * The memory limit also applies to the ensemble creation process. tmp_folder : string, optional (None) folder to store configuration output and log files, if ``None`` @@ -253,13 +257,15 @@ def __init__( n_jobs : int, optional, experimental The number of jobs to run in parallel for ``fit()``. ``-1`` means - using all processors. By default, Auto-sklearn uses a single core - for fitting the machine learning model and a single core for fitting - an ensemble. Ensemble building is not affected by ``n_jobs`` but - can be controlled by the number of models in the ensemble. In - contrast to most scikit-learn models, ``n_jobs`` given in the - constructor is not applied to the ``predict()`` method. If - ``dask_client`` is None, a new dask client is created. + using all processors. + + **Important notes**: + + * By default, Auto-sklearn uses one core. + * Ensemble building is not affected by ``n_jobs`` but can be controlled by the number + of models in the ensemble. + * ``predict()`` is not affected by ``n_jobs`` (in contrast to most scikit-learn models) + * If ``dask_client`` is ``None``, a new dask client is created. dask_client : dask.distributed.Client, optional User-created dask client, can be used to start a dask cluster and then @@ -275,7 +281,7 @@ def __init__( * ``'y_optimization'`` : do not save the predictions for the optimization/validation set, which would later on be used to build an ensemble. - * ``'model'`` : do not save any model files + * ``model`` : do not save any model files smac_scenario_args : dict, optional (None) Additional arguments inserted into the scenario of SMAC. See the diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv index 29e87b202f..2b990c9d14 100644 --- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv +++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv +++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv +++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv +++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv @@ -1,98 +1,98 @@ 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a/autosklearn/metalearning/files/r2_regression_dense/configurations.csv +++ b/autosklearn/metalearning/files/r2_regression_dense/configurations.csv @@ -1,98 +1,98 @@ 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+285,no_encoding,no_coalescense,,mean,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2186105871515939,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.10377482408306521,0.016255400771699312,least_squares,255,None,65,70,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,feature_type diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index 5cccd31267..79f5626d71 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -184,7 +184,7 @@ def _calculate(self, X, y, logger, categorical): def _calculate_sparse(self, X, y, logger, categorical): data = [True if not np.isfinite(x) else False for x in X.data] missing = X.__class__((data, X.indices, X.indptr), shape=X.shape, - dtype=np.bool) + dtype=bool) return missing diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 45f8b44ae0..13653de528 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -1,3 +1,5 @@ +from collections import OrderedDict + import numpy as np import pandas as pd @@ -39,7 +41,7 @@ def __init__(self, configuration_space, aslib_directory, logger): aslib_reader = aslib_simple.AlgorithmSelectionProblem(self.aslib_directory) self.metafeatures = aslib_reader.metafeatures - self.algorithm_runs = aslib_reader.algorithm_runs + self.algorithm_runs: OrderedDict[str, pd.DataFrame] = aslib_reader.algorithm_runs self.configurations = aslib_reader.configurations configurations = dict() @@ -65,7 +67,7 @@ def add_dataset(self, name, metafeatures): self.metafeatures.drop(name.lower(), inplace=True) self.metafeatures = self.metafeatures.append(metafeatures) - runs = pd.Series([], name=name) + runs = pd.Series([], name=name, dtype=float) for metric in self.algorithm_runs.keys(): self.algorithm_runs[metric].append(runs) diff --git a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py index 6092343a7a..ec9ea141c8 100644 --- a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py +++ b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py @@ -111,7 +111,8 @@ def _learn(self, exclude_double_configurations=True): except KeyError: # TODO should I really except this? self.logger.info("Could not find runs for instance %s" % task_id) - runs[task_id] = pd.Series([], name=task_id) + runs[task_id] = pd.Series([], name=task_id, dtype=float) + runs = pd.DataFrame(runs) kND.fit(all_other_metafeatures, runs) diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 34fb029b8a..cb6920979f 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -1,5 +1,6 @@ from abc import ABCMeta, abstractmethod from functools import partial +from itertools import product from typing import Any, Callable, Dict, List, Optional, Union, cast import numpy as np @@ -278,16 +279,14 @@ def make_scorer( optimum=0, worst_possible_result=MAXINT, greater_is_better=False) -r2 = make_scorer('r2', - sklearn.metrics.r2_score) + +r2 = make_scorer('r2', sklearn.metrics.r2_score) # Standard Classification Scores accuracy = make_scorer('accuracy', sklearn.metrics.accuracy_score) balanced_accuracy = make_scorer('balanced_accuracy', sklearn.metrics.balanced_accuracy_score) -f1 = make_scorer('f1', - sklearn.metrics.f1_score) # Score functions that need decision values roc_auc = make_scorer('roc_auc', @@ -297,10 +296,20 @@ def make_scorer( average_precision = make_scorer('average_precision', sklearn.metrics.average_precision_score, needs_threshold=True) -precision = make_scorer('precision', - sklearn.metrics.precision_score) -recall = make_scorer('recall', - sklearn.metrics.recall_score) + +# NOTE: zero_division +# +# Specified as the explicit default, see sklearn docs: +# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score +precision = make_scorer( + 'precision', partial(sklearn.metrics.precision_score, zero_division=0) +) +recall = make_scorer( + 'recall', partial(sklearn.metrics.recall_score, zero_division=0) +) +f1 = make_scorer( + 'f1', partial(sklearn.metrics.f1_score, zero_division=0) +) # Score function for probabilistic classification log_loss = make_scorer('log_loss', @@ -312,29 +321,39 @@ def make_scorer( # TODO what about mathews correlation coefficient etc? -REGRESSION_METRICS = dict() -for scorer in [mean_absolute_error, mean_squared_error, root_mean_squared_error, - mean_squared_log_error, median_absolute_error, r2]: - REGRESSION_METRICS[scorer.name] = scorer - -CLASSIFICATION_METRICS = dict() - -for scorer in [accuracy, balanced_accuracy, roc_auc, average_precision, - log_loss]: - CLASSIFICATION_METRICS[scorer.name] = scorer - -for name, metric in [('precision', sklearn.metrics.precision_score), - ('recall', sklearn.metrics.recall_score), - ('f1', sklearn.metrics.f1_score)]: - globals()[name] = make_scorer(name, metric) - CLASSIFICATION_METRICS[name] = globals()[name] - for average in ['macro', 'micro', 'samples', 'weighted']: - qualified_name = '{0}_{1}'.format(name, average) - globals()[qualified_name] = make_scorer(qualified_name, - partial(metric, - pos_label=None, - average=average)) - CLASSIFICATION_METRICS[qualified_name] = globals()[qualified_name] +REGRESSION_METRICS = { + scorer.name: scorer + for scorer in [ + mean_absolute_error, mean_squared_error, root_mean_squared_error, + mean_squared_log_error, median_absolute_error, r2 + ] +} + +CLASSIFICATION_METRICS = { + scorer.name: scorer + for scorer in [ + accuracy, balanced_accuracy, roc_auc, average_precision, log_loss + ] +} + +# NOTE: zero_division +# +# Specified as the explicit default, see sklearn docs: +# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score +for (base_name, sklearn_metric), average in product( + [ + ('precision', sklearn.metrics.precision_score), + ('recall', sklearn.metrics.recall_score), + ('f1', sklearn.metrics.f1_score), + ], + ['macro', 'micro', 'samples', 'weighted'] +): + name = f'{base_name}_{average}' + scorer = make_scorer( + name, partial(sklearn_metric, pos_label=None, average=average, zero_division=0) + ) + globals()[name] = scorer # Adds scorer to the module scope + CLASSIFICATION_METRICS[name] = scorer def calculate_score( diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index 3e02f7d4d8..5864a2a5d6 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -147,13 +147,16 @@ def __str__(self): class IterativeComponent(AutoSklearnComponent): + def fit(self, X, y, sample_weight=None): self.iterative_fit(X, y, n_iter=2, refit=True) + iteration = 2 while not self.configuration_fully_fitted(): n_iter = int(2 ** iteration / 2) self.iterative_fit(X, y, n_iter=n_iter, refit=False) iteration += 1 + return self @staticmethod @@ -165,15 +168,16 @@ def get_current_iter(self): class IterativeComponentWithSampleWeight(AutoSklearnComponent): + def fit(self, X, y, sample_weight=None): - self.iterative_fit( - X, y, n_iter=2, refit=True, sample_weight=sample_weight - ) + self.iterative_fit(X, y, n_iter=2, refit=True, sample_weight=sample_weight) + iteration = 2 while not self.configuration_fully_fitted(): n_iter = int(2 ** iteration / 2) - self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight) + self.iterative_fit(X, y, n_iter=n_iter, refit=False, sample_weight=sample_weight) iteration += 1 + return self @staticmethod diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py index 4489c7b61a..3ebb411457 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/encoding.py @@ -27,7 +27,14 @@ def fit(self, X: PIPELINE_DATA_DTYPE, categories='auto', handle_unknown='use_encoded_value', unknown_value=-1, ) self.preprocessor.fit(X, y) - return self + return self + else: + # TODO sparse_encoding of negative labels + # + # The next step in the pipeline relies on positive labels + # Given a categorical column [[0], [-1]], the next step will fail + # unless we can fix this encoding + return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: if scipy.sparse.issparse(X): diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py index f0fbbbd53d..519155ea20 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py @@ -3,6 +3,7 @@ from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np +from scipy.sparse import spmatrix from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm @@ -28,24 +29,32 @@ def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'CategoricalImputation': import sklearn.impute - fill_value = None if hasattr(X, 'columns'): kind = X[X.columns[-1]].dtype.kind else: # Series, sparse and numpy have dtype # Only DataFrame does not kind = X.dtype.kind - if kind in ("i", "u", "f"): - # We do not want to impute a category with the default - # value (0 is the default) in case such default is in the - # train data already! - fill_value = 0 - unique = np.unique(X) - while fill_value in unique: - fill_value -= 1 + + fill_value: Optional[int] = None + + number_kinds = ("i", "u", "f") + if kind in number_kinds: + if isinstance(X, spmatrix): + # TODO negative labels + # + # Previously this was the behaviour and went + # unnoticed. Imputing negative labels results in + # the cateogircal shift step failing as the ordinal + # encoder can't fix negative labels. + # This is here to document the behaviour explicitly + fill_value = 0 + else: + fill_value = min(np.unique(X)) - 1 self.preprocessor = sklearn.impute.SimpleImputer( - strategy='constant', copy=False, fill_value=fill_value) + strategy='constant', copy=False, fill_value=fill_value + ) self.preprocessor.fit(X) return self diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py index 2d57053cd3..dc9c9c60ac 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py @@ -19,17 +19,27 @@ def __init__( ) -> None: self.preprocessor: Optional[BaseEstimator] = None - def fit(self, X: PIPELINE_DATA_DTYPE, y: Optional[PIPELINE_DATA_DTYPE] = None - ) -> 'AutoSklearnPreprocessingAlgorithm': + def fit( + self, + X: PIPELINE_DATA_DTYPE, + y: Optional[PIPELINE_DATA_DTYPE] = None + ) -> 'AutoSklearnPreprocessingAlgorithm': + if self.preprocessor is None: raise NotFittedError() + self.preprocessor.fit(X) + return self def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE: + if self.preprocessor is None: - raise NotImplementedError() - return self.preprocessor.transform(X) + raise NotFittedError() + + transformed_X = self.preprocessor.transform(X) + + return transformed_X @staticmethod def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index 00a641323a..12ff57c21d 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -1,3 +1,6 @@ +from typing import Optional, Union + +from numpy.random import RandomState from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter @@ -8,13 +11,23 @@ class RandomKitchenSinks(AutoSklearnPreprocessingAlgorithm): - def __init__(self, gamma, n_components, random_state=None): - """ Parameters: + def __init__( + self, + gamma: float, + n_components: int, + random_state: Optional[Union[int, RandomState]] = None + ) -> None: + """ + Parameters + ---------- gamma: float - Parameter of the rbf kernel to be approximated exp(-gamma * x^2) + Parameter of the rbf kernel to be approximated exp(-gamma * x^2) n_components: int - Number of components (output dimensionality) used to approximate the kernel + Number of components (output dimensionality) used to approximate the kernel + + random_state: Optional[int | RandomState] + The random state to pass to the underlying estimator """ self.gamma = gamma self.n_components = n_components @@ -27,7 +40,10 @@ def fit(self, X, Y=None): self.gamma = float(self.gamma) self.preprocessor = sklearn.kernel_approximation.RBFSampler( - self.gamma, self.n_components, self.random_state) + gamma=self.gamma, + n_components=self.n_components, + random_state=self.random_state + ) self.preprocessor.fit(X) return self diff --git a/autosklearn/pipeline/components/regression/adaboost.py b/autosklearn/pipeline/components/regression/adaboost.py index 9af0df2bdc..2eb58ae2ea 100644 --- a/autosklearn/pipeline/components/regression/adaboost.py +++ b/autosklearn/pipeline/components/regression/adaboost.py @@ -15,7 +15,7 @@ def __init__(self, n_estimators, learning_rate, loss, max_depth, random_state=No self.max_depth = max_depth self.estimator = None - def fit(self, X, Y): + def fit(self, X, y): import sklearn.ensemble import sklearn.tree @@ -32,7 +32,11 @@ def fit(self, X, Y): loss=self.loss, random_state=self.random_state ) - self.estimator.fit(X, Y) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + + self.estimator.fit(X, y) return self def predict(self, X): diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index dd642e6098..46dcac5d93 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -22,8 +22,8 @@ def __init__(self, n_iter, tol, alpha_1, alpha_2, lambda_1, lambda_2, self.threshold_lambda = threshold_lambda self.fit_intercept = fit_intercept - def fit(self, X, Y): - import sklearn.linear_model + def fit(self, X, y): + from sklearn.linear_model import ARDRegression self.n_iter = int(self.n_iter) self.tol = float(self.tol) @@ -34,20 +34,25 @@ def fit(self, X, Y): self.threshold_lambda = float(self.threshold_lambda) self.fit_intercept = check_for_bool(self.fit_intercept) - self.estimator = sklearn.linear_model.\ - ARDRegression(n_iter=self.n_iter, - tol=self.tol, - alpha_1=self.alpha_1, - alpha_2=self.alpha_2, - lambda_1=self.lambda_1, - lambda_2=self.lambda_2, - compute_score=False, - threshold_lambda=self.threshold_lambda, - fit_intercept=True, - normalize=False, - copy_X=False, - verbose=False) - self.estimator.fit(X, Y) + self.estimator = ARDRegression( + n_iter=self.n_iter, + tol=self.tol, + alpha_1=self.alpha_1, + alpha_2=self.alpha_2, + lambda_1=self.lambda_1, + lambda_2=self.lambda_2, + compute_score=False, + threshold_lambda=self.threshold_lambda, + fit_intercept=True, + normalize=False, + copy_X=False, + verbose=False + ) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + + self.estimator.fit(X, y) return self def predict(self, X): diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index f458fbb9a5..5ecbd254be 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -56,6 +56,10 @@ def fit(self, X, y, sample_weight=None): min_weight_fraction_leaf=self.min_weight_fraction_leaf, min_impurity_decrease=self.min_impurity_decrease, random_state=self.random_state) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y, sample_weight=sample_weight) return self diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index 9b55205372..a676f0483d 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -95,7 +95,10 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.estimator.n_estimators = min(self.estimator.n_estimators, self.n_estimators) - self.estimator.fit(X, y,) + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + + self.estimator.fit(X, y) return self diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index 84a7fde238..c587b13b0e 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -10,10 +10,8 @@ def __init__(self, alpha, thetaL, thetaU, random_state=None): self.alpha = alpha self.thetaL = thetaL self.thetaU = thetaU - # We ignore it self.random_state = random_state self.estimator = None - self.scaler = None def fit(self, X, y): import sklearn.gaussian_process @@ -25,7 +23,8 @@ def fit(self, X, y): n_features = X.shape[1] kernel = sklearn.gaussian_process.kernels.RBF( length_scale=[1.0]*n_features, - length_scale_bounds=[(self.thetaL, self.thetaU)]*n_features) + length_scale_bounds=[(self.thetaL, self.thetaU)]*n_features + ) # Instanciate a Gaussian Process model self.estimator = sklearn.gaussian_process.GaussianProcessRegressor( @@ -35,9 +34,14 @@ def fit(self, X, y): alpha=self.alpha, copy_X_train=True, random_state=self.random_state, - normalize_y=True) + normalize_y=True + ) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() self.estimator.fit(X, y) + return self def predict(self, X): diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index 731a0e0da1..ad57596b9a 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -48,10 +48,7 @@ def get_current_iter(self): return self.estimator.n_iter_ def iterative_fit(self, X, y, n_iter=2, refit=False): - - """ - Set n_iter=2 for the same reason as for SGD - """ + """ Set n_iter=2 for the same reason as for SGD """ import sklearn.ensemble from sklearn.experimental import enable_hist_gradient_boosting # noqa @@ -112,6 +109,9 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): self.estimator.max_iter = min(self.estimator.max_iter, self.max_iter) + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y) if ( diff --git a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py index c8e92985ac..e4943e2ca5 100644 --- a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py @@ -13,7 +13,7 @@ def __init__(self, n_neighbors, weights, p, random_state=None): self.p = p self.random_state = random_state - def fit(self, X, Y): + def fit(self, X, y): import sklearn.neighbors self.n_neighbors = int(self.n_neighbors) @@ -24,7 +24,11 @@ def fit(self, X, Y): n_neighbors=self.n_neighbors, weights=self.weights, p=self.p) - self.estimator.fit(X, Y) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + + self.estimator.fit(X, y) return self def predict(self, X): diff --git a/autosklearn/pipeline/components/regression/liblinear_svr.py b/autosklearn/pipeline/components/regression/liblinear_svr.py index 043ef2ec82..73c1550ff3 100644 --- a/autosklearn/pipeline/components/regression/liblinear_svr.py +++ b/autosklearn/pipeline/components/regression/liblinear_svr.py @@ -23,7 +23,7 @@ def __init__(self, loss, epsilon, dual, tol, C, fit_intercept, self.random_state = random_state self.estimator = None - def fit(self, X, Y): + def fit(self, X, y): import sklearn.svm self.C = float(self.C) @@ -42,7 +42,11 @@ def fit(self, X, Y): fit_intercept=self.fit_intercept, intercept_scaling=self.intercept_scaling, random_state=self.random_state) - self.estimator.fit(X, Y) + + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + + self.estimator.fit(X, y) return self def predict(self, X): diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index 6be08d87ad..6b6c70415c 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -2,11 +2,10 @@ import sys from ConfigSpace.configuration_space import ConfigurationSpace -from ConfigSpace.conditions import InCondition +from ConfigSpace.conditions import InCondition, EqualsCondition from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter - from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE from autosklearn.util.common import check_for_bool, check_none @@ -29,7 +28,7 @@ def __init__(self, kernel, C, epsilon, tol, shrinking, gamma=0.1, self.random_state = random_state self.estimator = None - def fit(self, X, Y): + def fit(self, X, y): import sklearn.svm # Calculate the size of the kernel cache (in MB) for sklearn's LibSVM. The cache size is @@ -88,9 +87,19 @@ def fit(self, X, Y): ) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) - self.scaler.fit(Y.reshape((-1, 1))) - Y_scaled = self.scaler.transform(Y.reshape((-1, 1))).ravel() - self.estimator.fit(X, Y_scaled) + # Convert y to be at least 2d for the scaler + # [1,1,1] -> [[1], [1], [1]] + if y.ndim == 1: + y = y.reshape((-1, 1)) + + y_scaled = self.scaler.fit_transform(y) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if y_scaled.ndim == 2 and y_scaled.shape[1] == 1: + y_scaled = y_scaled.flatten() + + self.estimator.fit(X, y_scaled) + return self def predict(self, X): @@ -98,8 +107,15 @@ def predict(self, X): raise NotImplementedError if self.scaler is None: raise NotImplementedError - Y_pred = self.estimator.predict(X) - return self.scaler.inverse_transform(Y_pred) + y_pred = self.estimator.predict(X) + + inverse = self.scaler.inverse_transform(y_pred) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if inverse.ndim == 2 and inverse.shape[1] == 1: + inverse = inverse.flatten() + + return inverse @staticmethod def get_properties(dataset_properties=None): @@ -147,13 +163,12 @@ def get_hyperparameter_search_space(dataset_properties=None): cs.add_hyperparameters([C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, epsilon]) - degree_depends_on_kernel = InCondition(child=degree, parent=kernel, - values=('poly', 'rbf', 'sigmoid')) + degree_depends_on_poly = EqualsCondition(degree, kernel, "poly") gamma_depends_on_kernel = InCondition(child=gamma, parent=kernel, values=('poly', 'rbf')) coef0_depends_on_kernel = InCondition(child=coef0, parent=kernel, values=('poly', 'sigmoid')) - cs.add_conditions([degree_depends_on_kernel, gamma_depends_on_kernel, + cs.add_conditions([degree_depends_on_poly, gamma_depends_on_kernel, coef0_depends_on_kernel]) return cs diff --git a/autosklearn/pipeline/components/regression/mlp.py b/autosklearn/pipeline/components/regression/mlp.py index 198cbb8356..8eec40a2cc 100644 --- a/autosklearn/pipeline/components/regression/mlp.py +++ b/autosklearn/pipeline/components/regression/mlp.py @@ -137,16 +137,36 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): # max_fun=self.max_fun ) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) - self.scaler.fit(y.reshape((-1, 1))) + + # Convert y to be at least 2d for the StandardScaler + # [1,1,1] -> [[1], [1], [1]] + if y.ndim == 1: + y = y.reshape((-1, 1)) + + self.scaler.fit(y) else: new_max_iter = min(self.max_iter - self.estimator.n_iter_, n_iter) self.estimator.max_iter = new_max_iter - Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() - self.estimator.fit(X, Y_scaled) - if self.estimator.n_iter_ >= self.max_iter or \ - self.estimator._no_improvement_count > self.n_iter_no_change: + # Convert y to be at least 2d for the scaler + # [1,1,1] -> [[1], [1], [1]] + if y.ndim == 1: + y = y.reshape((-1, 1)) + + y_scaled = self.scaler.transform(y) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if y_scaled.ndim == 2 and y_scaled.shape[1] == 1: + y_scaled = y_scaled.flatten() + + self.estimator.fit(X, y_scaled) + + if ( + self.estimator.n_iter_ >= self.max_iter + or self.estimator._no_improvement_count > self.n_iter_no_change + ): self._fully_fit = True + return self def configuration_fully_fitted(self): @@ -160,8 +180,16 @@ def configuration_fully_fitted(self): def predict(self, X): if self.estimator is None: raise NotImplementedError - Y_pred = self.estimator.predict(X) - return self.scaler.inverse_transform(Y_pred) + + y_pred = self.estimator.predict(X) + + inverse = self.scaler.inverse_transform(y_pred) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if inverse.ndim == 2 and inverse.shape[1] == 1: + inverse = inverse.flatten() + + return inverse @staticmethod def get_properties(dataset_properties=None): diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 054c283dc5..eeaddb9e1a 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -85,6 +85,9 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.estimator.n_estimators = min(self.estimator.n_estimators, self.n_estimators) + if y.ndim == 2 and y.shape[1] == 1: + y = y.flatten() + self.estimator.fit(X, y) return self diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index e3bbf2b12a..8b3e7dbd34 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -90,17 +90,36 @@ def iterative_fit(self, X, y, n_iter=2, refit=False): warm_start=True) self.scaler = sklearn.preprocessing.StandardScaler(copy=True) - self.scaler.fit(y.reshape((-1, 1))) - Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() - self.estimator.fit(X, Y_scaled) + + if y.ndim == 1: + y = y.reshape((-1, 1)) + + y_scaled = self.scaler.fit_transform(y) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if y_scaled.ndim == 2 and y_scaled.shape[1] == 1: + y_scaled = y_scaled.flatten() + + self.estimator.fit(X, y_scaled) self.n_iter_ = self.estimator.n_iter_ else: self.estimator.max_iter += n_iter self.estimator.max_iter = min(self.estimator.max_iter, self.max_iter) - Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() + + # Convert y to be at least 2d for the scaler + # [1,1,1] -> [[1], [1], [1]] + if y.ndim == 1: + y = y.reshape((-1, 1)) + + y_scaled = self.scaler.transform(y) + + # Flatten: [[0], [0], [0]] -> [0, 0, 0] + if y_scaled.ndim == 2 and y_scaled.shape[1] == 1: + y_scaled = y_scaled.flatten() + self.estimator._validate_params() self.estimator._partial_fit( - X, Y_scaled, + X, y_scaled, alpha=self.estimator.alpha, C=1.0, loss=self.estimator.loss, diff --git a/autosklearn/py.typed b/autosklearn/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index dff29d84d0..3cb823f2ff 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -1,3 +1,4 @@ +from typing import Dict, List, Optional import copy import json import logging @@ -231,8 +232,8 @@ def __init__(self, config_space, dataset_name, metadata_directory=None, resampling_strategy='holdout', resampling_strategy_args=None, - include=None, - exclude=None, + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, disable_file_output=False, smac_scenario_args=None, get_smac_object_callback=None, @@ -435,6 +436,7 @@ def run_smbo(self): total_walltime_limit = self.total_walltime_limit - startup_time - 5 scenario_dict = { 'abort_on_first_run_crash': False, + 'save-results-instantly': True, 'cs': self.config_space, 'cutoff_time': self.func_eval_time_limit, 'deterministic': 'true', diff --git a/autosklearn/util/backend.py b/autosklearn/util/backend.py deleted file mode 100644 index a3265d3211..0000000000 --- a/autosklearn/util/backend.py +++ /dev/null @@ -1,425 +0,0 @@ -import glob -import os -import pickle -import shutil -import tempfile -import time -import uuid -import warnings -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np - -from sklearn.pipeline import Pipeline - -from autosklearn.data.abstract_data_manager import AbstractDataManager -from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble -from autosklearn.util.logging_ import PicklableClientLogger, get_named_client_logger - - -__all__ = [ - 'Backend' -] - - -def create( - temporary_directory: str, - delete_tmp_folder_after_terminate: bool = True, -) -> 'Backend': - context = BackendContext(temporary_directory, - delete_tmp_folder_after_terminate, - ) - backend = Backend(context) - - return backend - - -def get_randomized_directory_name(temporary_directory: Optional[str] = None) -> str: - uuid_str = str(uuid.uuid1(clock_seq=os.getpid())) - - temporary_directory = ( - temporary_directory - if temporary_directory - else os.path.join( - tempfile.gettempdir(), - "autosklearn_tmp_{}".format( - uuid_str, - ), - ) - ) - - return temporary_directory - - -class BackendContext(object): - - def __init__(self, - temporary_directory: str, - delete_tmp_folder_after_terminate: bool, - ): - - self.delete_tmp_folder_after_terminate = delete_tmp_folder_after_terminate - # attributes to check that directories were created by autosklearn. - self._tmp_dir_created = False - - self._temporary_directory = ( - get_randomized_directory_name( - temporary_directory=temporary_directory, - ) - ) - # Auto-Sklearn logs through the use of a PicklableClientLogger - # For this reason we need a port to communicate with the server - # When the backend is created, this port is not available - # When the port is available in the main process, we - # call the setup_logger with this port and update self.logger - self.logger = None # type: Optional[PicklableClientLogger] - self.create_directories() - - def setup_logger(self, port: int) -> None: - self._logger = get_named_client_logger( - name=__name__, - port=port, - ) - - @property - def temporary_directory(self) -> str: - # make sure that tilde does not appear on the path. - return os.path.expanduser(os.path.expandvars(self._temporary_directory)) - - def create_directories(self) -> None: - # Exception is raised if self.temporary_directory already exists. - os.makedirs(self.temporary_directory) - self._tmp_dir_created = True - - def delete_directories(self, force: bool = True) -> None: - - if self.delete_tmp_folder_after_terminate or force: - if self._tmp_dir_created is False: - raise ValueError("Failed to delete tmp dir: % s because auto-sklearn did not " - "create it. Please make sure that the specified tmp dir does not " - "exist when instantiating auto-sklearn." - % self.temporary_directory) - try: - shutil.rmtree(self.temporary_directory) - except Exception: - try: - if self._logger is not None: - self._logger.warning( - "Could not delete tmp dir: %s" % self.temporary_directory) - else: - print("Could not delete tmp dir: %s" % self.temporary_directory) - except Exception: - print("Could not delete tmp dir: %s" % self.temporary_directory) - - -class Backend(object): - """Utility class to load and save all objects to be persisted. - - These are: - * start time of auto-sklearn - * true targets of the ensemble - """ - - def __init__(self, context: BackendContext): - # When the backend is created, this port is not available - # When the port is available in the main process, we - # call the setup_logger with this port and update self.logger - self.logger = None # type: Optional[PicklableClientLogger] - self.context = context - - # Create the temporary directory if it does not yet exist - try: - os.makedirs(self.temporary_directory) - except Exception: - pass - - self.internals_directory = os.path.join(self.temporary_directory, ".auto-sklearn") - self._make_internals_directory() - - def setup_logger(self, port: int) -> None: - self.logger = get_named_client_logger( - name=__name__, - port=port, - ) - self.context.setup_logger(port) - - @property - def temporary_directory(self) -> str: - return self.context.temporary_directory - - def _make_internals_directory(self) -> None: - try: - os.makedirs(self.internals_directory) - except Exception as e: - if self.logger is not None: - self.logger.debug("_make_internals_directory: %s" % e) - try: - os.makedirs(self.get_runs_directory()) - except Exception as e: - if self.logger is not None: - self.logger.debug("_make_internals_directory: %s" % e) - - def _get_start_time_filename(self, seed: Union[str, int]) -> str: - if isinstance(seed, str): - seed = int(seed) - return os.path.join(self.internals_directory, "start_time_%d" % seed) - - def save_start_time(self, seed: str) -> str: - self._make_internals_directory() - start_time = time.time() - - filepath = self._get_start_time_filename(seed) - - if not isinstance(start_time, float): - raise ValueError("Start time must be a float, but is %s." % type(start_time)) - - if os.path.exists(filepath): - raise ValueError( - "{filepath} already exist. Different seeds should be provided for different jobs." - ) - - with tempfile.NamedTemporaryFile('w', dir=os.path.dirname(filepath), delete=False) as fh: - fh.write(str(start_time)) - tempname = fh.name - os.rename(tempname, filepath) - - return filepath - - def load_start_time(self, seed: int) -> float: - with open(self._get_start_time_filename(seed), 'r') as fh: - start_time = float(fh.read()) - return start_time - - def get_smac_output_directory(self) -> str: - return os.path.join(self.temporary_directory, 'smac3-output') - - def get_smac_output_directory_for_run(self, seed: int) -> str: - return os.path.join( - self.temporary_directory, - 'smac3-output', - 'run_%d' % seed - ) - - def _get_targets_ensemble_filename(self) -> str: - return os.path.join(self.internals_directory, - "true_targets_ensemble.npy") - - def save_targets_ensemble(self, targets: np.ndarray) -> str: - self._make_internals_directory() - if not isinstance(targets, np.ndarray): - raise ValueError('Targets must be of type np.ndarray, but is %s' % - type(targets)) - - filepath = self._get_targets_ensemble_filename() - - # Try to open the file without locking it, this will reduce the - # number of times where we erroneously keep a lock on the ensemble - # targets file although the process already was killed - try: - existing_targets = np.load(filepath, allow_pickle=True) - if existing_targets.shape[0] > targets.shape[0] or \ - (existing_targets.shape == targets.shape and - np.allclose(existing_targets, targets)): - - return filepath - except Exception: - pass - - with tempfile.NamedTemporaryFile('wb', dir=os.path.dirname( - filepath), delete=False) as fh_w: - np.save(fh_w, targets.astype(np.float32)) - tempname = fh_w.name - - os.rename(tempname, filepath) - - return filepath - - def load_targets_ensemble(self) -> np.ndarray: - filepath = self._get_targets_ensemble_filename() - - with open(filepath, 'rb') as fh: - targets = np.load(fh, allow_pickle=True) - - return targets - - def _get_datamanager_pickle_filename(self) -> str: - return os.path.join(self.internals_directory, 'datamanager.pkl') - - def save_datamanager(self, datamanager: AbstractDataManager) -> str: - self._make_internals_directory() - filepath = self._get_datamanager_pickle_filename() - - with tempfile.NamedTemporaryFile('wb', dir=os.path.dirname( - filepath), delete=False) as fh: - pickle.dump(datamanager, fh, -1) - tempname = fh.name - os.rename(tempname, filepath) - - return filepath - - def load_datamanager(self) -> AbstractDataManager: - filepath = self._get_datamanager_pickle_filename() - with open(filepath, 'rb') as fh: - return pickle.load(fh) - - def get_runs_directory(self) -> str: - return os.path.join(self.internals_directory, 'runs') - - def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str: - return os.path.join(self.internals_directory, 'runs', '%d_%d_%s' % (seed, num_run, budget)) - - def get_model_filename(self, seed: int, idx: int, budget: float) -> str: - return '%s.%s.%s.model' % (seed, idx, budget) - - def get_cv_model_filename(self, seed: int, idx: int, budget: float) -> str: - return '%s.%s.%s.cv_model' % (seed, idx, budget) - - def list_all_models(self, seed: int) -> List[str]: - runs_directory = self.get_runs_directory() - model_files = glob.glob( - os.path.join(glob.escape(runs_directory), '%d_*' % seed, '%s.*.*.model' % seed) - ) - return model_files - - def load_models_by_identifiers(self, identifiers: List[Tuple[int, int, float]] - ) -> Dict: - models = dict() - - for identifier in identifiers: - seed, idx, budget = identifier - models[identifier] = self.load_model_by_seed_and_id_and_budget( - seed, idx, budget) - - return models - - def load_model_by_seed_and_id_and_budget(self, seed: int, - idx: int, - budget: float - ) -> Pipeline: - model_directory = self.get_numrun_directory(seed, idx, budget) - - model_file_name = '%s.%s.%s.model' % (seed, idx, budget) - model_file_path = os.path.join(model_directory, model_file_name) - with open(model_file_path, 'rb') as fh: - return pickle.load(fh) - - def load_cv_models_by_identifiers(self, identifiers: List[Tuple[int, int, float]] - ) -> Dict: - models = dict() - - for identifier in identifiers: - seed, idx, budget = identifier - models[identifier] = self.load_cv_model_by_seed_and_id_and_budget( - seed, idx, budget) - - return models - - def load_cv_model_by_seed_and_id_and_budget(self, - seed: int, - idx: int, - budget: float - ) -> Pipeline: - model_directory = self.get_numrun_directory(seed, idx, budget) - - model_file_name = '%s.%s.%s.cv_model' % (seed, idx, budget) - model_file_path = os.path.join(model_directory, model_file_name) - with open(model_file_path, 'rb') as fh: - return pickle.load(fh) - - def save_numrun_to_dir( - self, seed: int, idx: int, budget: float, model: Optional[Pipeline], - cv_model: Optional[Pipeline], ensemble_predictions: Optional[np.ndarray], - valid_predictions: Optional[np.ndarray], test_predictions: Optional[np.ndarray], - ) -> None: - runs_directory = self.get_runs_directory() - tmpdir = tempfile.mkdtemp(dir=runs_directory) - if model is not None: - file_path = os.path.join(tmpdir, self.get_model_filename(seed, idx, budget)) - with open(file_path, 'wb') as fh: - pickle.dump(model, fh, -1) - - if cv_model is not None: - file_path = os.path.join(tmpdir, self.get_cv_model_filename(seed, idx, budget)) - with open(file_path, 'wb') as fh: - pickle.dump(cv_model, fh, -1) - - for preds, subset in ( - (ensemble_predictions, 'ensemble'), - (valid_predictions, 'valid'), - (test_predictions, 'test') - ): - if preds is not None: - file_path = os.path.join( - tmpdir, - self.get_prediction_filename(subset, seed, idx, budget) - ) - with open(file_path, 'wb') as fh: - pickle.dump(preds.astype(np.float32), fh, -1) - try: - os.rename(tmpdir, self.get_numrun_directory(seed, idx, budget)) - except OSError: - if os.path.exists(self.get_numrun_directory(seed, idx, budget)): - os.rename(self.get_numrun_directory(seed, idx, budget), - os.path.join(runs_directory, tmpdir + '.old')) - os.rename(tmpdir, self.get_numrun_directory(seed, idx, budget)) - shutil.rmtree(os.path.join(runs_directory, tmpdir + '.old')) - - def get_ensemble_dir(self) -> str: - return os.path.join(self.internals_directory, 'ensembles') - - def load_ensemble(self, seed: int) -> Optional[AbstractEnsemble]: - ensemble_dir = self.get_ensemble_dir() - - if not os.path.exists(ensemble_dir): - if self.logger is not None: - self.logger.warning('Directory %s does not exist' % ensemble_dir) - else: - warnings.warn('Directory %s does not exist' % ensemble_dir) - return None - - if seed >= 0: - indices_files = glob.glob( - os.path.join(glob.escape(ensemble_dir), '%s.*.ensemble' % seed) - ) - indices_files.sort() - else: - indices_files = os.listdir(ensemble_dir) - indices_files = [os.path.join(ensemble_dir, f) for f in indices_files] - indices_files.sort(key=lambda f: time.ctime(os.path.getmtime(f))) - - with open(indices_files[-1], 'rb') as fh: - ensemble_members_run_numbers = pickle.load(fh) - - return ensemble_members_run_numbers - - def save_ensemble(self, ensemble: AbstractEnsemble, idx: int, seed: int) -> None: - try: - os.makedirs(self.get_ensemble_dir()) - except Exception: - pass - - filepath = os.path.join( - self.get_ensemble_dir(), - '%s.%s.ensemble' % (str(seed), str(idx).zfill(10)) - ) - with tempfile.NamedTemporaryFile('wb', dir=os.path.dirname( - filepath), delete=False) as fh: - pickle.dump(ensemble, fh) - tempname = fh.name - os.rename(tempname, filepath) - - def get_prediction_filename(self, subset: str, - automl_seed: Union[str, int], - idx: int, - budget: float - ) -> str: - return 'predictions_%s_%s_%s_%s.npy' % (subset, automl_seed, idx, budget) - - def write_txt_file(self, filepath: str, data: str, name: str) -> None: - with tempfile.NamedTemporaryFile('w', dir=os.path.dirname( - filepath), delete=False) as fh: - fh.write(data) - tempname = fh.name - os.rename(tempname, filepath) - if self.logger is not None: - self.logger.debug('Created %s file %s' % (name, filepath)) diff --git a/autosklearn/util/data.py b/autosklearn/util/data.py index b344dc50bd..288485f1cc 100644 --- a/autosklearn/util/data.py +++ b/autosklearn/util/data.py @@ -53,7 +53,7 @@ def convert_to_bin(Ycont: List, nval: int, verbose: bool = True) -> List: Ybin = [[0] * nval for x in range(len(Ycont))] for i in range(len(Ybin)): line = Ybin[i] - line[np.int(Ycont[i])] = 1 + line[int(Ycont[i])] = 1 Ybin[i] = line return Ybin diff --git a/autosklearn/util/logging.yaml b/autosklearn/util/logging.yaml index 8c8bad3243..046778d0e6 100644 --- a/autosklearn/util/logging.yaml +++ b/autosklearn/util/logging.yaml @@ -33,7 +33,7 @@ loggers: level: DEBUG handlers: [file_handler] - autosklearn.util.backend: + autosklearn.automl_common.utils.backend: level: DEBUG handlers: [file_handler] propagate: no diff --git a/autosklearn/util/pipeline.py b/autosklearn/util/pipeline.py index 4a75d479d3..c1f5a2ca23 100755 --- a/autosklearn/util/pipeline.py +++ b/autosklearn/util/pipeline.py @@ -1,9 +1,9 @@ # -*- encoding: utf-8 -*- -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Union from ConfigSpace.configuration_space import ConfigurationSpace -from sklearn.pipeline import Pipeline +import numpy as np from autosklearn.constants import ( BINARY_CLASSIFICATION, @@ -16,27 +16,69 @@ from autosklearn.pipeline.regression import SimpleRegressionPipeline -__all__ = [ - 'get_configuration_space', - 'get_class', -] +__all__ = ['get_configuration_space'] -def get_configuration_space(info: Dict[str, Any], - include: Optional[Dict[str, List[str]]] = None, - exclude: Optional[Dict[str, List[str]]] = None, - ) -> ConfigurationSpace: +def get_configuration_space( + info: Dict[str, Any], + include: Optional[Dict[str, List[str]]] = None, + exclude: Optional[Dict[str, List[str]]] = None, + random_state: Optional[Union[int, np.random.RandomState]] = None +) -> ConfigurationSpace: + """Get the configuration of a pipeline given some dataset info + Parameters + ---------- + info: Dict[str, Any] + Information about the dataset + + include: Optional[Dict[str, List[str]]] = None + A dictionary of what components to include for each pipeline step + + exclude: Optional[Dict[str, List[str]]] = None + A dictionary of what components to exclude for each pipeline step + + random_state: Optional[Union[int, np.random.Randomstate]] = None + The random state to use for seeding the ConfigSpace + + Returns + ------- + ConfigurationSpace + The configuration space for the pipeline + """ if info['task'] in REGRESSION_TASKS: - return _get_regression_configuration_space(info, include, exclude) + return _get_regression_configuration_space(info, include, exclude, random_state) else: - return _get_classification_configuration_space(info, include, exclude) + return _get_classification_configuration_space(info, include, exclude, random_state) + +def _get_regression_configuration_space( + info: Dict[str, Any], + include: Optional[Dict[str, List[str]]], + exclude: Optional[Dict[str, List[str]]], + random_state: Optional[Union[int, np.random.RandomState]] = None +) -> ConfigurationSpace: + """Get the configuration of a regression pipeline given some dataset info -def _get_regression_configuration_space(info: Dict[str, Any], - include: Optional[Dict[str, List[str]]], - exclude: Optional[Dict[str, List[str]]] - ) -> ConfigurationSpace: + Parameters + ---------- + info: Dict[str, Any] + Information about the dataset + + include: Optional[Dict[str, List[str]]] = None + A dictionary of what components to include for each pipeline step + + exclude: Optional[Dict[str, List[str]]] = None + A dictionary of what components to exclude for each pipeline step + + random_state: Optional[Union[int, np.random.Randomstate]] = None + The random state to use for seeding the ConfigSpace + + Returns + ------- + ConfigurationSpace + The configuration space for the regression pipeline + """ task_type = info['task'] sparse = False multioutput = False @@ -54,15 +96,39 @@ def _get_regression_configuration_space(info: Dict[str, Any], configuration_space = SimpleRegressionPipeline( dataset_properties=dataset_properties, include=include, - exclude=exclude + exclude=exclude, + random_state=random_state ).get_hyperparameter_search_space() return configuration_space -def _get_classification_configuration_space(info: Dict[str, Any], - include: Optional[Dict[str, List[str]]], - exclude: Optional[Dict[str, List[str]]] - ) -> ConfigurationSpace: +def _get_classification_configuration_space( + info: Dict[str, Any], + include: Optional[Dict[str, List[str]]], + exclude: Optional[Dict[str, List[str]]], + random_state: Optional[Union[int, np.random.RandomState]] = None +) -> ConfigurationSpace: + """Get the configuration of a classification pipeline given some dataset info + + Parameters + ---------- + info: Dict[str, Any] + Information about the dataset + + include: Optional[Dict[str, List[str]]] = None + A dictionary of what components to include for each pipeline step + + exclude: Optional[Dict[str, List[str]]] = None + A dictionary of what components to exclude for each pipeline step + + random_state: Optional[Union[int, np.random.Randomstate]] = None + The random state to use for seeding the ConfigSpace + + Returns + ------- + ConfigurationSpace + The configuration space for the classification pipeline + """ task_type = info['task'] multilabel = False @@ -87,12 +153,7 @@ def _get_classification_configuration_space(info: Dict[str, Any], return SimpleClassificationPipeline( dataset_properties=dataset_properties, - include=include, exclude=exclude).\ - get_hyperparameter_search_space() - - -def get_class(info: Dict[str, Any]) -> Pipeline: - if info['task'] in REGRESSION_TASKS: - return SimpleRegressionPipeline - else: - return SimpleClassificationPipeline + include=include, + exclude=exclude, + random_state=random_state + ).get_hyperparameter_search_space() diff --git a/doc/Makefile b/doc/Makefile index 3683cf45a8..24165e787b 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -60,7 +60,7 @@ html: @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." html-noexamples: - $(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html + SPHINX_GALLERY_PLOT=False $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." @@ -167,7 +167,7 @@ changes: @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: - $(SPHINXBUILD) -D plot_gallery=0 -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + SPHINX_GALLERY_PLOT=False $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." diff --git a/doc/conf.py b/doc/conf.py index 2da16696f1..5d114b3550 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -43,6 +43,7 @@ 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx_gallery.gen_gallery', 'sphinx.ext.autosectionlabel', + 'sphinx_toolbox.collapse', # sphinx.ext.autosexctionlabel raises duplicate label warnings # because same section headers are used multiple times throughout # the documentation. @@ -67,6 +68,22 @@ if "dev" in autosklearn.__version__: binder_branch = "development" +# Getting issues with the `-D plot_gallery=0` for sphinx gallery, this is a workaround +# We do this by setting an evironment variable we check and modifying the python config +# object. +# We have this extra processing as it enters as a raw string and we need a boolean value +gallery_env_var ="SPHINX_GALLERY_PLOT" + +sphinx_plot_gallery_flag = True +if gallery_env_var in os.environ: + value = os.environ[gallery_env_var] + if value in ["False", "false", "0"]: + sphinx_plot_gallery_flag = False + elif value in ["True", "true", "1"]: + sphinx_plot_gallery_flag = True + else: + raise ValueError(f'Env variable {gallery_env_var} must be set to "false" or "true"') + sphinx_gallery_conf = { # path to the examples 'examples_dirs': '../examples', @@ -77,6 +94,7 @@ #'reference_url': { # 'autosklearn': None #}, + 'plot_gallery': sphinx_plot_gallery_flag, 'backreferences_dir': None, 'filename_pattern': 'example.*.py$', 'ignore_pattern': r'custom_metrics\.py|__init__\.py|example_parallel_manual_spawning_python.py', diff --git a/doc/faq.rst b/doc/faq.rst index d562eadc06..439e5c9be3 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -6,267 +6,518 @@ FAQ === -Issues -====== +General +======= -Auto-sklearn is extremely memory hungry in a sequential setting ---------------------------------------------------------------- +.. collapse:: Where can I find examples on how to use auto-sklearn? -Auto-sklearn can appear very memory hungry (i.e. requiring a lot of memory for small datasets) due -to the use of ``fork`` for creating new processes when running in sequential manner (if this -happens in a parallel setting or if you pass your own dask client this is due to a different -issue, see the other issues below). + We provide examples on using *auto-sklearn* for multiple use cases ranging from + simple classification to advanced uses such as feature importance, parallel runs + and customization. They can be found in the :ref:`sphx_glr_examples`. -Let's go into some more detail and discuss how to fix it: -Auto-sklearn executes each machine learning algorithm in its own process to be able to apply a -memory limit and a time limit. To start such a process, Python gives three options: ``fork``, -``forkserver`` and ``spawn``. The default ``fork`` copies the whole process memory into the -subprocess. If the main process already uses 1.5GB of main memory and we apply a 3GB memory -limit to Auto-sklearn, executing a machine learning pipeline is limited to use at most 1.5GB. -We would have loved to use ``forkserver`` or ``spawn`` as the default option instead, which both -copy only relevant data into the subprocess and thereby alleaviate the issue of eating up a lot -of your main memory -(and also do not suffer from potential deadlocks as ``fork`` does, see -`here `_), -but they have the downside that code must be guarded by ``if __name__ == "__main__"`` or executed -in a notebook, and we decided that we do not want to require this by default. +.. collapse:: What type of tasks can auto-sklearn tackle? -There are now two possible solutions: + *auto-sklearn* can accept targets for the following tasks (more details on `Sklearn algorithms `_): -1. Use Auto-sklearn in parallel: if you use Auto-sklean in parallel, it defaults to ``forkserver`` - as the parallelization mechanism itself requires Auto-sklearn the code to be guarded. Please - find more information on how to do this in the following two examples: + * Binary Classification + * Multiclass Classification + * Multilabel Classification + * Regression + * Multioutput Regression - 1. :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py` - 2. :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py` + You can provide feature and target training pairs (X_train/y_train) to *auto-sklearn* to fit an + ensemble of pipelines as described in the next section. This X_train/y_train dataset must belong + to one of the supported formats: np.ndarray, pd.DataFrame, scipy.sparse.csr_matrix and python lists. + Optionally, you can measure the ability of this fitted model to generalize to unseen data by + providing an optional testing pair (X_test/Y_test). For further details, please refer to the + Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. + Supported formats for these training and testing pairs are: np.ndarray, + pd.DataFrame, scipy.sparse.csr_matrix and python lists. - .. note:: + If your data contains categorical values (in the features or targets), autosklearn will automatically encode your + data using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ + for multidimensional data. - This requires all code to be guarded by ``if __name__ == "__main__"``. + Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: -2. Pass a `dask client `_. If the user passes - a dask client, Auto-sklearn can no longer assume that it runs in sequential mode and will use - a ``forkserver`` to start new processes. + * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you + can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. + * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the + column has a categorical/boolean class, it will be encoded. If the column is of any other type + (Object or Timeseries), an error will be raised. For further details on how to properly encode + your data, you can check the Pandas Example + `Working with categorical data `_). + If you are working with time series, it is recommended that you follow this approach + `Working with time data `_. - .. note:: + Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be + automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding + is created between these splits (if only y_train is provided during fit, the categorical encoder will not be able + to handle new classes that are exclusive to y_test). If the task is regression, no encoding happens on the + targets. - This requires all code to be guarded by ``if __name__ == "__main__"``. +.. collapse:: Where can I find slides and notebooks from talks and tutorials? -We therefore suggest using one of the above settings by default. + We provide resources for talks, tutorials and presentations on *auto-sklearn* under `auto-sklearn-talks `_ -Auto-sklearn is extremely memory hungry in a parallel setting -------------------------------------------------------------- +.. collapse:: How should I cite auto-sklearn in a scientific publication? -When running Auto-sklearn in a parallel setting it starts new processes for evaluating machine -learning models using the ``forkserver`` mechanism. Code that is in the main script and that is -not guarded by ``if __name__ == "__main__"`` will be executed for each subprocess. If, for example, -you are loading your dataset outside of the guarded code, your dataset will be loaded for each -evaluation of a machine learning algorithm and thus blocking your RAM. + If you've used auto-sklearn in scientific publications, we would appreciate citations. -We therefore suggest moving all code inside functions or the main block. + .. code-block:: -Auto-sklearn crashes with a segmentation fault ----------------------------------------------- + @inproceedings{feurer-neurips15a, + title = {Efficient and Robust Automated Machine Learning}, + author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina Springenberg, Jost and Blum, Manuel and Hutter, Frank}, + booktitle = {Advances in Neural Information Processing Systems 28 (2015)}, + pages = {2962--2970}, + year = {2015} + } -Please make sure that you have read and followed the :ref:`installation` section! In case -everything is set up correctly, this is most likely due to the dependency -`pyrfr `_ not being compiled correctly. If this is the -case please execute: + Or this, if you've used auto-sklearn 2.0 in your work: -.. code:: python + .. code-block:: - import pyrfr.regression as reg - data = reg.default_data_container(64) + @article{feurer-arxiv20a, + title = {Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning}, + author = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank}, + booktitle = {arXiv:2007.04074 [cs.LG]}, + year = {2020} + } -If this fails, the pyrfr dependency is most likely not compiled correctly. We advice you to do the -following: +.. collapse:: I want to contribute. What can I do? -1. Check if you can use a pre-compiled version of the pyrfr to avoid compiling it yourself. We - provide pre-compiled versions of the pyrfr on `pypi `_. -2. Check if the dependencies specified under :ref:`installation` are correctly installed, - especially that you have ``swig`` and a ``C++`` compiler. -3. If you are not yet using Conda, consider using it; it simplifies installation of the correct - dependencies. -4. Install correct build dependencies before installing the pyrfr, you can check the following - github issues for suggestions: `1025 `_, - `856 `_ + This sounds great. Please have a look at our `contribution guide `_ -Log files and output -==================== +.. collapse:: I have a question which is not answered here. What should I do? -Where does Auto-sklearn output files by default? ------------------------------------------------- + Thanks a lot. We regularly update this section with questions from our issue tracker. So please use the + `issue tracker `_ -*Auto-sklearn* heavily uses the hard drive to store temporary data, models and log files which can -be used to inspect the behavior of Auto-sklearn. Each run of Auto-sklearn requires -its own directory. If not provided by the user, *Auto-sklearn* requests a temporary directory from -Python, which by default is located under ``/tmp`` and starts with ``autosklearn_tmp_`` followed -by a random string. By default, this directory is deleted when the *Auto-sklearn* object is -destroyed. If you want to keep these files you can pass the argument -``delete_tmp_folder_after_terminate=True`` to the *Auto-sklearn* object. +Resource Management +=================== -The :class:`autosklearn.classification.AutoSklearnClassifier` and all other *auto-sklearn* -estimators accept the argument ``tmp_directory`` which change where such output is written to. +.. collapse:: How should I set the time and memory limits? -There's an additional argument ``output_directory`` which can be passed to *Auto-sklearn* and it -controls where test predictions of the ensemble are stored if the test set is passed to ``fit()``. + While *auto-sklearn* alleviates manual hyperparameter tuning, the user still + has to set memory and time limits. For most datasets a memory limit of 3GB or + 6GB as found on most modern computers is sufficient. For the time limits it + is harder to give clear guidelines. If possible, a good default is a total + time limit of one day, and a time limit of 30 minutes for a single run. -Auto-sklearn eats up all my disk space --------------------------------------- + Further guidelines can be found in + `auto-sklearn/issues/142 `_. -*Auto-sklearn* heavily uses the hard drive to store temporary data, models and log files which can -be used to inspect the behavior of Auto-sklearn. By default, *Auto-sklearn* stores 50 -models and their predictions on the validation data (which is a subset of the training data in -case of holdout and the full training data in case of cross-validation) on the hard drive. -Redundant models and their predictions (i.e. when we have more than 50 models) are removed -everytime the ensemble builder finishes an iteration, which means that the number of models stored -on disk can temporarily be higher if a model is output while the ensemble builder is running. +.. collapse:: How many CPU cores does auto-sklearn use by default? -One can therefore change the number of models that will be stored on disk by passing an integer -for the argument ``max_models_on_disc`` to *Auto-sklearn*, for example reduce the number of models -stored on disk if you have space issues. + By default, *auto-sklearn* uses **one core**. See also :ref:`parallel` on how to configure this. -As the number of models is only an indicator of the disk space used it is also possible to pass -the memory in MB the models are allowed to use as a ``float`` (also via the ``max_models_on_disc`` -arguments). As above, this is rather a guideline on how much memory is used as redundant models -are only removed from disk when the ensemble builder finishes an iteration. +.. collapse:: How can I run auto-sklearn in parallel? -.. note:: + Nevertheless, *auto-sklearn* also supports parallel Bayesian optimization via the use of + `Dask.distributed `_. By providing the arguments ``n_jobs`` + to the estimator construction, one can control the number of cores available to *auto-sklearn* + (As shown in the Example :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py`). + Distributed processes are also supported by providing a custom client object to *auto-sklearn* like + in the Example: :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py`. When + multiple cores are + available, *auto-sklearn* will create a worker per core, and use the available workers to both search + for better machine learning models as well as building an ensemble with them until the time resource + is exhausted. - Especially when running in parallel it can happen that multiple models are constructed during - one run of the ensemble builder and thus *Auto-sklearn* can exceed the given limit. + **Note:** *auto-sklearn* requires all workers to have access to a shared file system for storing training data and models. -.. note:: + *auto-sklearn* employs `threadpoolctl `_ to control the number of threads employed by scientific libraries like numpy or scikit-learn. This is done exclusively during the building procedure of models, not during inference. In particular, *auto-sklearn* allows each pipeline to use at most 1 thread during training. At predicting and scoring time this limitation is not enforced by *auto-sklearn*. You can control the number of resources + employed by the pipelines by setting the following variables in your environment, prior to running *auto-sklearn*: - These limits do only apply to models and their predictions, but not to other files stored in - the temporary directory such as the log files. + .. code-block:: shell-session -Available machine learning models -================================= + $ export OPENBLAS_NUM_THREADS=1 + $ export MKL_NUM_THREADS=1 + $ export OMP_NUM_THREADS=1 -Will non-scikit-learn models be added to Auto-sklearn? ------------------------------------------------------- + For further information about how scikit-learn handles multiprocessing, please check the `Parallelism, resource management, and configuration `_ documentation from the library. -The short answer: no. -The long answer answer is a bit more nuanced: maintaining Auto-sklearn requires a lot of time and -effort, which would grow even larger when depending on more libraries. Also, adding more -libraries would require us to generate meta-data more often. Lastly, having more choices does not -guarantee a better performance for most users as having more choices demands a longer search for -good models and can lead to more overfitting. +.. collapse:: Auto-sklearn is extremely memory hungry in a sequential setting -Nevertheless, everyone can still add their favorite model to Auto-sklearn's search space by -following the `examples on how to extend Auto-sklearn -`_. + Auto-sklearn can appear very memory hungry (i.e. requiring a lot of memory for small datasets) due + to the use of ``fork`` for creating new processes when running in sequential manner (if this + happens in a parallel setting or if you pass your own dask client this is due to a different + issue, see the other issues below). -If there is interest in creating a Auto-sklearn-contrib repository with 3rd-party models please -open an issue for that. + Let's go into some more detail and discuss how to fix it: + Auto-sklearn executes each machine learning algorithm in its own process to be able to apply a + memory limit and a time limit. To start such a process, Python gives three options: ``fork``, + ``forkserver`` and ``spawn``. The default ``fork`` copies the whole process memory into the + subprocess. If the main process already uses 1.5GB of main memory and we apply a 3GB memory + limit to Auto-sklearn, executing a machine learning pipeline is limited to use at most 1.5GB. + We would have loved to use ``forkserver`` or ``spawn`` as the default option instead, which both + copy only relevant data into the subprocess and thereby alleaviate the issue of eating up a lot + of your main memory + (and also do not suffer from potential deadlocks as ``fork`` does, see + `here `_), + but they have the downside that code must be guarded by ``if __name__ == "__main__"`` or executed + in a notebook, and we decided that we do not want to require this by default. -Can the preprocessing be disabled ---------------------------------- + There are now two possible solutions: -Feature preprocessing can be disabled as discussed in the example -:ref:`restricting_the_searchspace`. Other preprocessing steps such as one hot encoding, missing -feature imputation and normalization cannot yet be disabled, but we're working on that. + 1. Use Auto-sklearn in parallel: if you use Auto-sklean in parallel, it defaults to ``forkserver`` + as the parallelization mechanism itself requires Auto-sklearn the code to be guarded. Please + find more information on how to do this in the following two examples: -Usage -===== + 1. :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py` + 2. :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py` + + .. note:: + + This requires all code to be guarded by ``if __name__ == "__main__"``. + + 2. Pass a `dask client `_. If the user passes + a dask client, Auto-sklearn can no longer assume that it runs in sequential mode and will use + a ``forkserver`` to start new processes. + + .. note:: + + This requires all code to be guarded by ``if __name__ == "__main__"``. + + We therefore suggest using one of the above settings by default. + +.. collapse:: Auto-sklearn is extremely memory hungry in a parallel setting + + When running Auto-sklearn in a parallel setting it starts new processes for evaluating machine + learning models using the ``forkserver`` mechanism. Code that is in the main script and that is + not guarded by ``if __name__ == "__main__"`` will be executed for each subprocess. If, for example, + you are loading your dataset outside of the guarded code, your dataset will be loaded for each + evaluation of a machine learning algorithm and thus blocking your RAM. + + We therefore suggest moving all code inside functions or the main block. + +.. collapse:: Auto-sklearn crashes with a segmentation fault + + Please make sure that you have read and followed the :ref:`installation` section! In case + everything is set up correctly, this is most likely due to the dependency + `pyrfr `_ not being compiled correctly. If this is the + case please execute: + + .. code:: python + + import pyrfr.regression as reg + data = reg.default_data_container(64) + + If this fails, the pyrfr dependency is most likely not compiled correctly. We advice you to do the + following: + + 1. Check if you can use a pre-compiled version of the pyrfr to avoid compiling it yourself. We + provide pre-compiled versions of the pyrfr on `pypi `_. + 2. Check if the dependencies specified under :ref:`installation` are correctly installed, + especially that you have ``swig`` and a ``C++`` compiler. + 3. If you are not yet using Conda, consider using it; it simplifies installation of the correct + dependencies. + 4. Install correct build dependencies before installing the pyrfr, you can check the following + github issues for suggestions: `1025 `_, + `856 `_ + +Results, Log Files and Output +============================= + +.. collapse:: How can I get an overview of the run statistics? + + ``sprint_statistics()`` is a method that prints the name of the dataset, the metric used, and the best validation score + obtained by running *auto-sklearn*. It additionally prints the number of both successful and unsuccessful + algorithm runs. + +.. collapse:: What was the performance over time? + + ``performance_over_time_`` returns a DataFrame containing the models performance over time data, which can + be used for plotting directly (Here is an example: :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`). + + .. code:: python + + automl.performance_over_time_.plot( + x='Timestamp', + kind='line', + legend=True, + title='Auto-sklearn accuracy over time', + grid=True, + ) + plt.show() + +.. collapse:: Which models were evaluated? + + You can see all models evaluated using :meth:`automl.leaderboard(ensemble_only=False) `. + +.. collapse:: Which models are in the final ensemble? + + Use either :meth:`automl.leaderboard(ensemble_only=True) ` or ``automl.show_models()`` + +.. collapse:: Is there more data I can look at? + + ``cv_results_`` returns a dict with keys as column headers and values as columns, that can be imported into + a pandas DataFrame, e.g. ``df = pd.DataFrame(automl.cv_results_)`` + +.. collapse:: Where does Auto-sklearn output files by default? + + *Auto-sklearn* heavily uses the hard drive to store temporary data, models and log files which can + be used to inspect the behavior of Auto-sklearn. Each run of Auto-sklearn requires + its own directory. If not provided by the user, *Auto-sklearn* requests a temporary directory from + Python, which by default is located under ``/tmp`` and starts with ``autosklearn_tmp_`` followed + by a random string. By default, this directory is deleted when the *Auto-sklearn* object is + finished fitting. If you want to keep these files you can pass the argument + ``delete_tmp_folder_after_terminate=True`` to the *Auto-sklearn* object. + + The :class:`autosklearn.classification.AutoSklearnClassifier` and all other *auto-sklearn* + estimators accept the argument ``tmp_folder`` which change where such output is written to. + + There's an additional argument ``output_directory`` which can be passed to *Auto-sklearn* and it + controls where test predictions of the ensemble are stored if the test set is passed to ``fit()``. + +.. collapse:: Auto-sklearn's logfiles eat up all my disk space. What can I do? + + *Auto-sklearn* heavily uses the hard drive to store temporary data, models and log files which can + be used to inspect the behavior of Auto-sklearn. By default, *Auto-sklearn* stores 50 + models and their predictions on the validation data (which is a subset of the training data in + case of holdout and the full training data in case of cross-validation) on the hard drive. + Redundant models and their predictions (i.e. when we have more than 50 models) are removed + everytime the ensemble builder finishes an iteration, which means that the number of models stored + on disk can temporarily be higher if a model is output while the ensemble builder is running. + + One can therefore change the number of models that will be stored on disk by passing an integer + for the argument ``max_models_on_disc`` to *Auto-sklearn*, for example reduce the number of models + stored on disk if you have space issues. + + As the number of models is only an indicator of the disk space used it is also possible to pass + the memory in MB the models are allowed to use as a ``float`` (also via the ``max_models_on_disc`` + arguments). As above, this is rather a guideline on how much memory is used as redundant models + are only removed from disk when the ensemble builder finishes an iteration. + + .. note:: + + Especially when running in parallel it can happen that multiple models are constructed during + one run of the ensemble builder and thus *Auto-sklearn* can exceed the given limit. + + .. note:: + + These limits do only apply to models and their predictions, but not to other files stored in + the temporary directory such as the log files. + +The Search Space +================ -Only use interpretable models ------------------------------ +.. collapse:: How can I restrict the searchspace? -Auto-sklearn can be restricted to only use interpretable models and preprocessing algorithms. -Please see the Section :ref:`restricting_the_searchspace` to learn how to restrict the models -which are searched over or see the Example -:ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`. + The following shows an example of how to exclude all preprocessing methods and restrict the configuration space to + only random forests. -We don't provide a judgement which of the models are interpretable as this is very much up to the -specific use case, but would like to note that decision trees and linear models usually most -interpretable. + .. code:: python -Limiting the number of model evaluations ----------------------------------------- + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier( + include = { + 'classifier': ["random_forest"], + 'feature_preprocessor': ["no_preprocessing"] + }, + exclude=None + ) + automl.fit(X_train, y_train) + predictions = automl.predict(X_test) -In certain cases, for example for debugging, it can be helpful to limit the number of -model evaluations. We do not provide this as an argument in the API as we believe that it -should NOT be used in practice, but that the user should rather provide time limits. -An example on how to add the number of models to try as an additional stopping condition -can be found `in this github issue `_. -Please note that Auto-sklearn will stop when either the time limit or the number of -models termination condition is reached. + **Note:** The strings used to identify estimators and preprocessors are the filenames without *.py*. -Ensemble contains only a dummy model ------------------------------------- + For a full list please have a look at the source code (in `autosklearn/pipeline/components/`): -This is a symptom of the problem that all runs started by Auto-sklearn failed. Usually, the issue -is that the runtime or memory limit were too tight. Please check the output of -``sprint_statistics`` to see the distribution of why runs failed. If there are mostly crashed -runs, please check the log file for further details. If there are mostly runs that exceed the -memory or time limit, please increase the respective limit and rerun the optimization. + * `Classifiers `_ + * `Regressors `_ + * `Preprocessors `_ -Parallel processing and oversubscription ----------------------------------------- + We do also provide an example on how to restrict the classifiers to search over + :ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`. -Auto-sklearn wraps scikit-learn and therefore inherits its parallelism implementation. In short, -scikit-learn uses two modes of parallelizing computations: +.. collapse:: How can I turn off data preprocessing? -1. By using joblib to distribute independent function calls on multiple cores. -2. By using lower level libraries such as OpenMP and numpy to distribute more fine-grained - computation. + Data preprocessing includes One-Hot encoding of categorical features, imputation + of missing values and the normalization of features or samples. These ensure that + the data the gets to the sklearn models is well formed and can be used for + training models. -This means that Auto-sklearn can use more resources than expected by the user. For technical -reasons we can only control the 1st way of parallel execution, but not the 2nd. Thus, the user -needs to make sure that the lower level parallelization libraries only use as many cores as -allocated (on a laptop or workstation running a single copy of Auto-sklearn it can be fine to not -adjust this, but when using a compute cluster it is necessary to align the parallelism setting -with the number of requested CPUs). This can be done by setting the following environment -variables: ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, ``BLIS_NUM_THREADS`` and -``OMP_NUM_THREADS``. + While this is necessary in general, if you'd like to disable this step, please + refer to this :ref:`example `. -More details can be found in the `scikit-learn docs `_. +.. collapse:: How can I turn off feature preprocessing? + + Feature preprocessing is a single transformer which implements for example feature + selection or transformation of features into a different space (i.e. PCA). + + This can be turned off by setting + ``include={'feature_preprocessor'=["no_preprocessing"]}`` as shown in the example above. + +.. collapse:: Will non-scikit-learn models be added to Auto-sklearn? + + The short answer: no. + + The long answer answer is a bit more nuanced: maintaining Auto-sklearn requires a lot of time and + effort, which would grow even larger when depending on more libraries. Also, adding more + libraries would require us to generate meta-data more often. Lastly, having more choices does not + guarantee a better performance for most users as having more choices demands a longer search for + good models and can lead to more overfitting. + + Nevertheless, everyone can still add their favorite model to Auto-sklearn's search space by + following the `examples on how to extend Auto-sklearn + `_. + + If there is interest in creating a Auto-sklearn-contrib repository with 3rd-party models please + open an issue for that. + +.. collapse:: How can I only search for interpretable models + + Auto-sklearn can be restricted to only use interpretable models and preprocessing algorithms. + Please see the Section :ref:`space` to learn how to restrict the models + which are searched over or see the Example + :ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`. + + We don't provide a judgement which of the models are interpretable as this is very much up to the + specific use case, but would like to note that decision trees and linear models usually most + interpretable. + +Ensembling +========== + +.. collapse:: What can I configure wrt the ensemble building process? + + The following hyperparameters control how the ensemble is constructed: + + * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. + * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) `_). + * ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled. + +.. collapse:: Which models are in the final ensemble? + + The results obtained from the final ensemble can be printed by calling ``show_models()`` or ``leaderboard()``. + The *auto-sklearn* ensemble is composed of scikit-learn models that can be inspected as exemplified + in the Example :ref:`sphx_glr_examples_40_advanced_example_get_pipeline_components.py`. + +.. collapse:: Can I fit an ensemble also only post-hoc? + + It is possible to build ensembles post-hoc. An example on how to do this (first searching for individual models, and then building an ensemble from them) can be seen in :ref:`sphx_glr_examples_60_search_example_sequential.py`. + +Configuring the Search Procedure +================================ + +.. collapse:: Can I change the resampling strategy? + + Examples for using holdout and cross-validation can be found in :ref:`example ` + +.. collapse:: Can I use a custom metric + + Examples for using a custom metric can be found in :ref:`example ` Meta-Learning ============= -Which datasets are used for meta-learning? ------------------------------------------- +.. collapse:: Which datasets are used for meta-learning? + + We updated the list of datasets used for meta-learning several times and this list now differs + significantly from the original 140 datasets we used in 2015 when the paper and the package were + released. An up-to-date list of `OpenML task IDs `_ can be found + on `github `_. + +.. collapse:: How can datasets from the meta-data be excluded? + + For *Auto-sklearn 1.0* one can pass the dataset name via the ``fit()`` function. If a dataset + with the same name is within the meta-data, that datasets will not be used. + + For *Auto-sklearn 2.0* it is not possible to do so because of the method used to construct the + meta-data. + +.. collapse:: Which meta-features are used for meta-learning? -We updated the list of datasets used for meta-learning several times and this list now differs -significantly from the original 140 datasets we used in 2015 when the paper and the package were -released. An up-to-date list of `OpenML task IDs `_ can be found -on `github `_. + We do not have a user guide on meta-features but they are all pretty simple and can be found + `in the source code `_. -How can datasets from the meta-data be excluded? ------------------------------------------------- +.. collapse:: How is the meta-data generated for Auto-sklearn 1.0? -For *Auto-sklearn 1.0* one can pass the dataset name via the ``fit()`` function. If a dataset -with the same name is within the meta-data, that datasets will not be used. + We currently generate meta-data the following way. First, for each of the datasets mentioned + above, we run Auto-sklearn without meta-learning for a total of two days on multiple metrics (for + classification these are accuracy, balanced accuracy, log loss and the area under the curce). + Second, for each run we then have a look at each models that improved the score, i.e. the + trajectory of the best known model at a time, and refit it on the whole training data. Third, for + each of these models we then compute all scores we're interested in, these also include other + ones such F1 and precision. Finally, for each combination of dataset and metric we store the best + model we know of. + +.. collapse:: How is the meta-data generated for Auto-sklearn 2.0? + + Please check `our paper `_ for details. + + +Issues and Debugging +==================== -For *Auto-sklearn 2.0* it is not possible to do so because of the method used to construct the -meta-data. +.. collapse:: How can I limit the number of model evaluations for debugging? + + In certain cases, for example for debugging, it can be helpful to limit the number of + model evaluations. We do not provide this as an argument in the API as we believe that it + should NOT be used in practice, but that the user should rather provide time limits. + An example on how to add the number of models to try as an additional stopping condition + can be found `in this github issue `_. + Please note that Auto-sklearn will stop when either the time limit or the number of + models termination condition is reached. + +.. collapse:: Why does the final ensemble contains only a dummy model? + + This is a symptom of the problem that all runs started by Auto-sklearn failed. Usually, the issue + is that the runtime or memory limit were too tight. Please check the output of + ``sprint_statistics()`` to see the distribution of why runs failed. If there are mostly crashed + runs, please check the log file for further details. If there are mostly runs that exceed the + memory or time limit, please increase the respective limit and rerun the optimization. + +.. collapse:: Auto-sklearn does not use the specified amount of resources? + + Auto-sklearn wraps scikit-learn and therefore inherits its parallelism implementation. In short, + scikit-learn uses two modes of parallelizing computations: + + 1. By using joblib to distribute independent function calls on multiple cores. + 2. By using lower level libraries such as OpenMP and numpy to distribute more fine-grained + computation. + + This means that Auto-sklearn can use more resources than expected by the user. For technical + reasons we can only control the 1st way of parallel execution, but not the 2nd. Thus, the user + needs to make sure that the lower level parallelization libraries only use as many cores as + allocated (on a laptop or workstation running a single copy of Auto-sklearn it can be fine to not + adjust this, but when using a compute cluster it is necessary to align the parallelism setting + with the number of requested CPUs). This can be done by setting the following environment + variables: ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, ``BLIS_NUM_THREADS`` and + ``OMP_NUM_THREADS``. + + More details can be found in the `scikit-learn docs `_. + +Other +===== -Which meta-features are used for meta-learning? ------------------------------------------------ +.. collapse:: Model persistence -We do not have a user guide on meta-features but they are all pretty simple and can be found -`in the source code `_. + *auto-sklearn* is mostly a wrapper around scikit-learn. Therefore, it is + possible to follow the + `persistence Example `_ + from scikit-learn. -How is the meta-data generated? -------------------------------- +.. collapse:: Vanilla auto-sklearn -Auto-sklearn 1.0 -~~~~~~~~~~~~~~~~ + In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning + `_ + set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: -We currently generate meta-data the following way. First, for each of the datasets mentioned -above, we run Auto-sklearn without meta-learning for a total of two days on multiple metrics (for -classification these are accuracy, balanced accuracy, log loss and the area under the curce). -Second, for each run we then have a look at each models that improved the score, i.e. the -trajectory of the best known model at a time, and refit it on the whole training data. Third, for -each of these models we then compute all scores we're interested in, these also include other -ones such F1 and precision. Finally, for each combination of dataset and metric we store the best -model we know of. + .. code:: python -Auto-sklearn 2.0 -~~~~~~~~~~~~~~~~ + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier( + ensemble_size=1, + initial_configurations_via_metalearning=0 + ) -Please check `our paper `_ for details. + An ensemble of size one will result in always choosing the current best model + according to its performance on the validation set. Setting the initial + configurations found by meta-learning to zero makes *auto-sklearn* use the + regular SMAC algorithm for suggesting new hyperparameter configurations. diff --git a/doc/index.rst b/doc/index.rst index c82cdb0eae..e0690ac8e7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -22,7 +22,7 @@ replacement for a scikit-learn estimator: hyperparameter tuning. It leverages recent advantages in *Bayesian optimization*, *meta-learning* and *ensemble construction*. Learn more about the technology behind *auto-sklearn* by reading our paper published at -`NIPS 2015 `_ +`NeurIPS 2015 `_ . .. topic:: NEW: Auto-sklearn 2.0 @@ -38,6 +38,11 @@ the technology behind *auto-sklearn* by reading our paper published at A paper describing our advances is available on `arXiv `_. +.. topic:: NEW: Material from tutorials and presentations + + We provide slides and notebooks from talks and tutorials here: `auto-sklearn-talks `_ + + Example ******* diff --git a/doc/installation.rst b/doc/installation.rst index dff3cecd36..544dd9fff5 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -21,8 +21,8 @@ need: * SWIG (`get SWIG here `_). -For an explanation of missing Microsoft Windows and MAC OSX support please -check the Section `Windows/OSX compatibility`_. +For an explanation of missing Microsoft Windows and macOS support please +check the Section `Windows/macOS compatibility`_. Installing auto-sklearn ======================= @@ -100,9 +100,33 @@ to read in more details check for more information about Conda forge check `conda-forge documentations `_. +Source Installation +=================== + +You can install auto-sklearn directly form source by following the below: + +.. code:: bash -Windows/OSX compatibility -========================= + git clone --recurse-submodules git@github.com:automl/auto-sklearn.git + cd auto-sklearn + + # Install it in editable mode with all optional dependencies + pip install -e ".[test,doc,examples]" + +We use submodules so you will have to make sure the submodule is initialized if you +missed the `--recurse-submodules` option. + +.. code:: bash + + git clone git@github.com:automl/auto-sklearn.git + cd auto-sklearn + git submodule update --init --recursive + + pip install -e ".[test,doc,examples]" + + +Windows/macOS compatibility +=========================== Windows ~~~~~~~ @@ -120,15 +144,15 @@ Possible solutions: * docker image -Mac OSX -~~~~~~~ +macOS +~~~~~ -We currently do not know if *auto-sklearn* works on OSX. There are at least two -issues holding us back from actively supporting OSX: +We currently do not know if *auto-sklearn* works on macOS. There are at least two +issues holding us back from actively supporting macOS: * The ``resource`` module cannot enforce a memory limit on a Python process (see `SMAC3/issues/115 `_). -* Not all dependencies we are using are set up to work on OSX. +* Not all dependencies we are using are set up to work on macOS. In case you're having issues installing the `pyrfr package `_, check out `this installation suggestion on github `_. diff --git a/doc/manual.rst b/doc/manual.rst index 252626666d..2a3df6528b 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -6,232 +6,299 @@ Manual ====== -This manual shows how to use several aspects of auto-sklearn. It either -references the examples where possible or explains certain configurations. +This manual gives an overview of different aspects of *auto-sklearn*. For each section, we either references examples or +give short explanations (click the title to expand text), e.g. -Examples -======== +.. collapse:: Code examples -We provide examples on using *auto-sklearn* for multiple use cases ranging from -simple classification to advanced uses such as feature importance, parallel runs -and customization. They can be found in the :ref:`sphx_glr_examples`. + We provide examples on using *auto-sklearn* for multiple use cases ranging from + simple classification to advanced uses such as feature importance, parallel runs + and customization. They can be found in the :ref:`sphx_glr_examples`. -Time and memory limits -====================== +.. collapse:: Material from talks and presentations -A crucial feature of *auto-sklearn* is limiting the resources (memory and -time) which the scikit-learn algorithms are allowed to use. Especially for -large datasets, on which algorithms can take several hours and make the -machine swap, it is important to stop the evaluations after some time in order -to make progress in a reasonable amount of time. Setting the resource limits -is therefore a tradeoff between optimization time and the number of models -that can be tested. + We provide resources for talks, tutorials and presentations on *auto-sklearn* under `auto-sklearn-talks `_ -While *auto-sklearn* alleviates manual hyperparameter tuning, the user still -has to set memory and time limits. For most datasets a memory limit of 3GB or -6GB as found on most modern computers is sufficient. For the time limits it -is harder to give clear guidelines. If possible, a good default is a total -time limit of one day, and a time limit of 30 minutes for a single run. +.. _limits: -Further guidelines can be found in -`auto-sklearn/issues/142 `_. +Resource limits +=============== -.. _restricting_the_searchspace: +A crucial feature of *auto-sklearn* is limiting the resources (memory and time) which the scikit-learn algorithms are +allowed to use. Especially for large datasets, on which algorithms can take several hours and make the machine swap, +it is important to stop the evaluations after some time in order to make progress in a reasonable amount of time. +Setting the resource limits is therefore a tradeoff between optimization time and the number of models that can be +tested. -Restricting the searchspace -=========================== +.. collapse:: Time and memory limits -Instead of using all available estimators, it is possible to restrict -*auto-sklearn*'s searchspace. The following shows an example of how to exclude -all preprocessing methods and restrict the configuration space to only -random forests. + While *auto-sklearn* alleviates manual hyperparameter tuning, the user still + has to set memory and time limits. For most datasets a memory limit of 3GB or + 6GB as found on most modern computers is sufficient. For the time limits it + is harder to give clear guidelines. If possible, a good default is a total + time limit of one day, and a time limit of 30 minutes for a single run. -.. code:: python + Further guidelines can be found in + `auto-sklearn/issues/142 `_. + +.. collapse:: CPU cores - import autosklearn.classification - automl = autosklearn.classification.AutoSklearnClassifier( - include = { - 'classifier': ["random_forest"], - 'feature_preprocessor': ["no_preprocessing"] - }, - exclude=None - ) - automl.fit(X_train, y_train) - predictions = automl.predict(X_test) + By default, *auto-sklearn* uses **one core**. See also :ref:`parallel` on how to configure this. -**Note:** The strings used to identify estimators and preprocessors are the filenames without *.py*. +.. _space: -For a full list please have a look at the source code (in `autosklearn/pipeline/components/`): +The search space +================ - * `Classifiers `_ - * `Regressors `_ - * `Preprocessors `_ +*Auto-sklearn* by default searches a large space to find a well performing configuration. However, it is also possible +to restrict the searchspace: -We do also provide an example on how to restrict the classifiers to search over -:ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`. +.. collapse:: Restricting the searchspace -Data preprocessing -~~~~~~~~~~~~~~~~~~ -Data preprocessing includes One-Hot encoding of categorical features, imputation -of missing values and the normalization of features or samples. These ensure that -the data the gets to the sklearn models is well formed and can be used for -training models. + The following shows an example of how to exclude all preprocessing methods and restrict the configuration space to + only random forests. -While this is necessary in general, if you'd like to disable this step, please -refer to this :ref:`example `. + .. code:: python -Feature preprocessing -~~~~~~~~~~~~~~~~~~~~~ -Feature preprocessing is a single transformer which implements for example feature -selection or transformation of features into a different space (i.e. PCA). + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier( + include = { + 'classifier': ["random_forest"], + 'feature_preprocessor': ["no_preprocessing"] + }, + exclude=None + ) + automl.fit(X_train, y_train) + predictions = automl.predict(X_test) -This can be turned off by setting -``include={'feature_preprocessor'=["no_preprocessing"]}`` as shown in the example above. + **Note:** The strings used to identify estimators and preprocessors are the filenames without *.py*. -Resampling strategies -===================== + For a full list please have a look at the source code (in `autosklearn/pipeline/components/`): -Examples for using holdout and cross-validation can be found in :ref:`auto-sklearn/examples/ `. + * `Classifiers `_ + * `Regressors `_ + * `Preprocessors `_ -Supported Inputs -================ -*auto-sklearn* can accept targets for the following tasks (more details on `Sklearn algorithms `_): + We do also provide an example on how to restrict the classifiers to search over + :ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py`. + +.. collapse:: Turn off data preprocessing + + Data preprocessing includes One-Hot encoding of categorical features, imputation + of missing values and the normalization of features or samples. These ensure that + the data the gets to the sklearn models is well formed and can be used for + training models. + + While this is necessary in general, if you'd like to disable this step, please + refer to this :ref:`example `. + +.. collapse:: Turn off feature preprocessing + + Feature preprocessing is a single transformer which implements for example feature + selection or transformation of features into a different space (i.e. PCA). + + This can be turned off by setting + ``include={'feature_preprocessor'=["no_preprocessing"]}`` as shown in the example above. + +.. _bestmodel: -* Binary Classification -* Multiclass Classification -* Multilabel Classification -* Regression -* Multioutput Regression +Model selection +=============== -You can provide feature and target training pairs (X_train/y_train) to *auto-sklearn* to fit an -ensemble of pipelines as described in the next section. This X_train/y_train dataset must belong -to one of the supported formats: np.ndarray, pd.DataFrame, scipy.sparse.csr_matrix and python lists. -Optionally, you can measure the ability of this fitted model to generalize to unseen data by -providing an optional testing pair (X_test/Y_test). For further details, please refer to the -Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. -Supported formats for these training and testing pairs are: np.ndarray, -pd.DataFrame, scipy.sparse.csr_matrix and python lists. +*Auto-sklearn* implements different strategies to identify the best performing model. For some use cases it might be +necessary to adapt the resampling strategy or define a custom metric: -If your data contains categorical values (in the features or targets), autosklearn will automatically encode your data using a `sklearn.preprocessing.LabelEncoder `_ for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ for multidimensional data. +.. collapse:: Use different resampling strategies -Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: + Examples for using holdout and cross-validation can be found in :ref:`example ` -* Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you - can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. -* You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical - dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the - column has a categorical/boolean class, it will be encoded. If the column is of any other type - (Object or Timeseries), an error will be raised. For further details on how to properly encode - your data, you can check the Pandas Example - `Working with categorical data `_). - If you are working with time series, it is recommended that you follow this approach - `Working with time data `_. +.. collapse:: Use a custom metric -Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding is created between these splits (if only y_train is provided during fit, the categorical encoder will not be able to handle new classes that are exclusive to y_test). If the task is regression, no encoding happens on the targets. + Examples for using a custom metric can be found in :ref:`example ` -Ensemble Building Process -========================= +.. _ensembles: -*auto-sklearn* uses ensemble selection by `Caruana et al. (2004) `_ -to build an ensemble based on the models’ prediction for the validation set. The following hyperparameters control how the ensemble is constructed: +Ensembling +========== -* ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. -* ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) `_). -* ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled. +To get the best performance out of the evaluated models, *auto-sklearn* uses ensemble selection by `Caruana et al. (2004) `_ +to build an ensemble based on the models’ prediction for the validation set. -.. _inspecting_the_results: +.. collapse:: Configure the ensemble building process + + The following hyperparameters control how the ensemble is constructed: + + * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. + * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) `_). + * ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled. + +.. collapse:: Inspect the final ensemble + + The results obtained from the final ensemble can be printed by calling ``show_models()``. + The *auto-sklearn* ensemble is composed of scikit-learn models that can be inspected as exemplified + in the Example :ref:`sphx_glr_examples_40_advanced_example_get_pipeline_components.py`. + +.. collapse:: Fit ensemble post-hoc + + To use a single core only, it is possible to build ensembles post-hoc. An example on how to do this (first searching + for individual models, and then building an ensemble from them) can be seen in + :ref:`sphx_glr_examples_60_search_example_sequential.py`. + + +.. _inspect: Inspecting the results ====================== -*auto-sklearn* allows users to inspect the training results and statistics. The following example shows how different -statistics can be printed for the inspection. +*auto-sklearn* allows users to inspect the training results and statistics. Assume we have a fitted estimator: .. code:: python - import autosklearn.classification - automl = autosklearn.classification.AutoSklearnClassifier() - automl.fit(X_train, y_train) - automl.cv_results_ - automl.performance_over_time_.plot( - x='Timestamp', - kind='line', - legend=True, - title='Auto-sklearn accuracy over time', - grid=True, - ) - plt.show() - - automl.sprint_statistics() - automl.show_models() - -``cv_results_`` returns a dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame. -``performance_over_time_`` returns a DataFrame containing the models performance over time data, which can be used for plotting directly (Here is an example: :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`). -``sprint_statistics()`` is a method that prints the name of the dataset, the metric used, and the best validation score -obtained by running *auto-sklearn*. It additionally prints the number of both successful and unsuccessful -algorithm runs. - -The results obtained from the final ensemble can be printed by calling ``show_models()``. -*auto-sklearn* ensemble is composed of scikit-learn models that can be inspected as exemplified -in the Example :ref:`sphx_glr_examples_40_advanced_example_get_pipeline_components.py`. + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier() + automl.fit(X_train, y_train) + +*auto-sklearn* offers the following ways to inspect the results + +.. collapse:: Basic statistics + + ``sprint_statistics()`` is a method that prints the name of the dataset, the metric used, and the best validation score + obtained by running *auto-sklearn*. It additionally prints the number of both successful and unsuccessful + algorithm runs. + +.. collapse:: Performance over Time + + ``performance_over_time_`` returns a DataFrame containing the models performance over time data, which can + be used for plotting directly (Here is an example: :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`). + + .. code:: python + + automl.performance_over_time_.plot( + x='Timestamp', + kind='line', + legend=True, + title='Auto-sklearn accuracy over time', + grid=True, + ) + plt.show() + +.. collapse:: Evaluated models + + The results obtained from the final ensemble can be printed by calling ``show_models()``. + +.. collapse:: Leaderboard + + ``automl.leaderboard()`` shows the ensemble members, check the :meth:`docs ` for using leaderboard for getting information on *all* runs. + +.. collapse:: Other + + ``cv_results_`` returns a dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame. + +.. _parallel: Parallel computation ==================== -In it's default mode, *auto-sklearn* already uses two cores. The first one is -used for model building, the second for building an ensemble every time a new -machine learning model has finished training. An example on how to do this sequentially (first searching for individual models, and then building an ensemble from them) can be seen in -:ref:`sphx_glr_examples_60_search_example_sequential.py`. +In it's default mode, *auto-sklearn* uses **one core** and interleaves ensemble building with evaluating new +configurations. -Nevertheless, *auto-sklearn* also supports parallel Bayesian optimization via the use of -`Dask.distributed `_. By providing the arguments ``n_jobs`` -to the estimator construction, one can control the number of cores available to *auto-sklearn* -(As shown in the Example :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py`). -Distributed processes are also supported by providing a custom client object to *auto-sklearn* like -in the Example: :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py`. When -multiple cores are -available, *auto-sklearn* will create a worker per core, and use the available workers to both search -for better machine learning models as well as building an ensemble with them until the time resource -is exhausted. +.. collapse:: Parallelization with Dask -**Note:** *auto-sklearn* requires all workers to have access to a shared file system for storing training data and models. + Nevertheless, *auto-sklearn* also supports parallel Bayesian optimization via the use of + `Dask.distributed `_. By providing the arguments ``n_jobs`` + to the estimator construction, one can control the number of cores available to *auto-sklearn* + (As shown in the Example :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py`). + Distributed processes are also supported by providing a custom client object to *auto-sklearn* like + in the Example: :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py`. When + multiple cores are + available, *auto-sklearn* will create a worker per core, and use the available workers to both search + for better machine learning models as well as building an ensemble with them until the time resource + is exhausted. -*auto-sklearn* employs `threadpoolctl `_ to control the number of threads employed by scientific libraries like numpy or scikit-learn. This is done exclusively during the building procedure of models, not during inference. In particular, *auto-sklearn* allows each pipeline to use at most 1 thread during training. At predicting and scoring time this limitation is not enforced by *auto-sklearn*. You can control the number of resources -employed by the pipelines by setting the following variables in your environment, prior to running *auto-sklearn*: + **Note:** *auto-sklearn* requires all workers to have access to a shared file system for storing training data and models. -.. code-block:: shell-session + *auto-sklearn* employs `threadpoolctl `_ to control the number of threads employed by scientific libraries like numpy or scikit-learn. This is done exclusively during the building procedure of models, not during inference. In particular, *auto-sklearn* allows each pipeline to use at most 1 thread during training. At predicting and scoring time this limitation is not enforced by *auto-sklearn*. You can control the number of resources + employed by the pipelines by setting the following variables in your environment, prior to running *auto-sklearn*: - $ export OPENBLAS_NUM_THREADS=1 - $ export MKL_NUM_THREADS=1 - $ export OMP_NUM_THREADS=1 + .. code-block:: shell-session + $ export OPENBLAS_NUM_THREADS=1 + $ export MKL_NUM_THREADS=1 + $ export OMP_NUM_THREADS=1 -For further information about how scikit-learn handles multiprocessing, please check the `Parallelism, resource management, and configuration `_ documentation from the library. -Model persistence -================= + For further information about how scikit-learn handles multiprocessing, please check the `Parallelism, resource management, and configuration `_ documentation from the library. -*auto-sklearn* is mostly a wrapper around scikit-learn. Therefore, it is -possible to follow the -`persistence Example `_ -from scikit-learn. +.. _othermanual: -Vanilla auto-sklearn -==================== +Other +===== -In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning -`_ -set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: +.. collapse:: Supported input types -.. code:: python + *auto-sklearn* can accept targets for the following tasks (more details on `Sklearn algorithms `_): + + * Binary Classification + * Multiclass Classification + * Multilabel Classification + * Regression + * Multioutput Regression + + You can provide feature and target training pairs (X_train/y_train) to *auto-sklearn* to fit an + ensemble of pipelines as described in the next section. This X_train/y_train dataset must belong + to one of the supported formats: np.ndarray, pd.DataFrame, scipy.sparse.csr_matrix and python lists. + Optionally, you can measure the ability of this fitted model to generalize to unseen data by + providing an optional testing pair (X_test/Y_test). For further details, please refer to the + Example :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`. + Supported formats for these training and testing pairs are: np.ndarray, + pd.DataFrame, scipy.sparse.csr_matrix and python lists. + + If your data contains categorical values (in the features or targets), autosklearn will automatically encode your + data using a `sklearn.preprocessing.LabelEncoder `_ + for unidimensional data and a `sklearn.preprocessing.OrdinalEncoder `_ + for multidimensional data. + + Regarding the features, there are two methods to guide *auto-sklearn* to properly encode categorical columns: + + * Providing a X_train/X_test numpy array with the optional flag feat_type. For further details, you + can check the Example :ref:`sphx_glr_examples_40_advanced_example_feature_types.py`. + * You can provide a pandas DataFrame, with properly formatted columns. If a column has numerical + dtype, *auto-sklearn* will not encode it and it will be passed directly to scikit-learn. If the + column has a categorical/boolean class, it will be encoded. If the column is of any other type + (Object or Timeseries), an error will be raised. For further details on how to properly encode + your data, you can check the Pandas Example + `Working with categorical data `_). + If you are working with time series, it is recommended that you follow this approach + `Working with time data `_. + + Regarding the targets (y_train/y_test), if the task involves a classification problem, such features will be + automatically encoded. It is recommended to provide both y_train and y_test during fit, so that a common encoding + is created between these splits (if only y_train is provided during fit, the categorical encoder will not be able + to handle new classes that are exclusive to y_test). If the task is regression, no encoding happens on the + targets. + +.. collapse:: Model persistence + + *auto-sklearn* is mostly a wrapper around scikit-learn. Therefore, it is + possible to follow the + `persistence Example `_ + from scikit-learn. + +.. collapse:: Vanilla auto-sklearn + + In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning + `_ + set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: + + .. code:: python - import autosklearn.classification - automl = autosklearn.classification.AutoSklearnClassifier( - ensemble_size=1, - initial_configurations_via_metalearning=0 - ) + import autosklearn.classification + automl = autosklearn.classification.AutoSklearnClassifier( + ensemble_size=1, + initial_configurations_via_metalearning=0 + ) -An ensemble of size one will result in always choosing the current best model -according to its performance on the validation set. Setting the initial -configurations found by meta-learning to zero makes *auto-sklearn* use the -regular SMAC algorithm for suggesting new hyperparameter configurations. + An ensemble of size one will result in always choosing the current best model + according to its performance on the validation set. Setting the initial + configurations found by meta-learning to zero makes *auto-sklearn* use the + regular SMAC algorithm for suggesting new hyperparameter configurations. diff --git a/doc/releases.rst b/doc/releases.rst index a96f4c4d67..bc7c33a4a1 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -9,6 +9,40 @@ Releases ======== +Version 0.14.4 +============== + +* Fix #1356: SVR degree hyperparameter now only active with "poly" kernel. +* Add #1311: Black format checking (non-strict). +* Maint #1306: Run history is now saved every iteration +* Doc #1309: Updated the doc faqs to include many use cases and the manual for early introductions +* Doc #1322: Fix typo in contribution guide +* Maint #1326: Add isort checker (non-strict) +* Maint #1238, #1346, #1368, #1370: Update warnings in tests +* Maint #1325: Test workflow can now be manually triggered +* Maint #1332: Update docstring and typing of ``include`` and ``exclude`` params +* Add #1260: Support for Python 3.10 +* Add #1318: First update to use the shared backend in a new submodule `automl_common `_ +* Fix #1339: Resolve dependancy issues with ``sphinx_toolbox`` +* Fix #1335: Fix issue where some regression algorithm gave incorrect output dimensions as raised in #1297 +* Doc #1340: Update example for predefined splits +* Fix #1329: Fix random state not being passed to the ConfigurationSpace +* Maint #1348: Stop double triggering of github workflows +* Doc #1349: Rename OSX to macOS in docs +* Add #1321: Change ``show_models()`` to produce actual pipeline objects and not a ``str`` +* Maint #1361: Remove ``flaky`` dependency +* Maint #1366: Make ``SimpleClassificationPipeline`` tests more deterministic +* Maint #1367: Update test values for ``MLPRegressor`` with newer numpy + +Contributors v0.14.4 +******************** + +* Eddie Bergman +* Matthias Feurer +* Katharina Eggensperger +* UserFindingSelf +* partev + Version 0.14.3 ============== @@ -631,7 +665,7 @@ Version 0.4.0 minimization problem. * Implements `#271 `_: XGBoost is available again, even configuring the new dropout functionality. -* New documentation section :ref:`inspecting_the_results`. +* New documentation section :ref:`inspect`. * Fixes `#444 `_: Auto-sklearn now only loads models for refit which are actually relevant for the ensemble. diff --git a/examples/20_basic/example_classification.py b/examples/20_basic/example_classification.py index 86fc09a5f4..fcb99b65ef 100644 --- a/examples/20_basic/example_classification.py +++ b/examples/20_basic/example_classification.py @@ -7,6 +7,8 @@ The following example shows how to fit a simple classification model with *auto-sklearn*. """ +from pprint import pprint + import sklearn.datasets import sklearn.metrics @@ -42,7 +44,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ########################################################################### # Get the Score of the final ensemble diff --git a/examples/20_basic/example_multilabel_classification.py b/examples/20_basic/example_multilabel_classification.py index b46caa2233..835b110ea6 100644 --- a/examples/20_basic/example_multilabel_classification.py +++ b/examples/20_basic/example_multilabel_classification.py @@ -8,6 +8,7 @@ `here `_. """ import numpy as np +from pprint import pprint import sklearn.datasets import sklearn.metrics @@ -30,7 +31,7 @@ # More information on: https://scikit-learn.org/stable/modules/multiclass.html y[y == 'TRUE'] = 1 y[y == 'FALSE'] = 0 -y = y.astype(np.int) +y = y.astype(int) # Using type of target is a good way to make sure your data # is properly formatted @@ -65,7 +66,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ############################################################################ # Print statistics about the auto-sklearn run diff --git a/examples/20_basic/example_multioutput_regression.py b/examples/20_basic/example_multioutput_regression.py index 5db733da0a..a2e345fcac 100644 --- a/examples/20_basic/example_multioutput_regression.py +++ b/examples/20_basic/example_multioutput_regression.py @@ -8,6 +8,7 @@ *auto-sklearn*. """ import numpy as numpy +from pprint import pprint from sklearn.datasets import make_regression from sklearn.metrics import r2_score @@ -46,7 +47,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ########################################################################### # Get the Score of the final ensemble diff --git a/examples/20_basic/example_regression.py b/examples/20_basic/example_regression.py index adfc390dab..6b47607db0 100644 --- a/examples/20_basic/example_regression.py +++ b/examples/20_basic/example_regression.py @@ -7,6 +7,8 @@ The following example shows how to fit a simple regression model with *auto-sklearn*. """ +from pprint import pprint + import sklearn.datasets import sklearn.metrics @@ -43,7 +45,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ##################################### # Get the Score of the final ensemble diff --git a/examples/40_advanced/example_get_pipeline_components.py b/examples/40_advanced/example_get_pipeline_components.py index 76132291fc..f7a97ead27 100644 --- a/examples/40_advanced/example_get_pipeline_components.py +++ b/examples/40_advanced/example_get_pipeline_components.py @@ -14,6 +14,8 @@ the sklearn models. This example illustrates how to interact with the sklearn components directly, in this case a PCA preprocessor. """ +from pprint import pprint + import sklearn.datasets import sklearn.metrics @@ -62,10 +64,17 @@ # `Ensemble Selection `_ # to construct ensembles in a post-hoc fashion. The ensemble is a linear # weighting of all models constructed during the hyperparameter optimization. -# This prints the final ensemble. It is a list of tuples, each tuple being -# the model weight in the ensemble and the model itself. - -print(automl.show_models()) +# This prints the final ensemble. It is a dictionary where ``model_id`` of +# each model is a key, and value is a dictionary containing information +# of that model. A model's dict contains its ``'model_id'``, ``'rank'``, +# ``'cost'``, ``'ensemble_weight'``, and the model itself. The model is +# given by the ``'data_preprocessor'``, ``'feature_preprocessor'``, +# ``'regressor'/'classifier'`` and ``'sklearn_regressor'/'sklearn_classifier'`` +# entries. But for the ``'cv'`` resampling strategy, the same for each cv +# model is stored in the ``'estimators'`` list in the dict, along with the +# ``'voting_model'``. + +pprint(automl.show_models(), indent=4) ########################################################################### # Report statistics about the search diff --git a/examples/40_advanced/example_interpretable_models.py b/examples/40_advanced/example_interpretable_models.py index a9a4e015c5..a78695082c 100644 --- a/examples/40_advanced/example_interpretable_models.py +++ b/examples/40_advanced/example_interpretable_models.py @@ -7,6 +7,8 @@ The following example shows how to inspect the models which *auto-sklearn* optimizes over and how to restrict them to an interpretable subset. """ +from pprint import pprint + import autosklearn.classification import sklearn.datasets import sklearn.metrics @@ -70,7 +72,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ########################################################################### # Get the Score of the final ensemble diff --git a/examples/40_advanced/example_resampling.py b/examples/40_advanced/example_resampling.py index 39e76cb481..124316a60a 100644 --- a/examples/40_advanced/example_resampling.py +++ b/examples/40_advanced/example_resampling.py @@ -98,8 +98,9 @@ # data by the first feature. In practice, one would use a splitting according # to the use case at hand. +selected_indices = (X_train[:, 0] < np.mean(X_train[:, 0])).astype(int) resampling_strategy = sklearn.model_selection.PredefinedSplit( - test_fold=np.where(X_train[:, 0] < np.mean(X_train[:, 0]))[0] + test_fold=selected_indices ) automl = autosklearn.classification.AutoSklearnClassifier( @@ -111,6 +112,8 @@ ) automl.fit(X_train, y_train, dataset_name='breast_cancer') +print(automl.sprint_statistics()) + ############################################################################ # For custom resampling strategies (i.e. resampling strategies that are not # defined as strings by Auto-sklearn) it is necessary to perform a refit: diff --git a/examples/60_search/example_random_search.py b/examples/60_search/example_random_search.py index 292f005da9..2c9cc76695 100644 --- a/examples/60_search/example_random_search.py +++ b/examples/60_search/example_random_search.py @@ -12,6 +12,7 @@ as yet another alternative optimizatino strategy. Both examples are intended to show how the optimization strategy in *auto-sklearn* can be adapted. """ # noqa (links are too long) +from pprint import pprint import sklearn.model_selection import sklearn.datasets @@ -75,7 +76,7 @@ def get_roar_object_callback( print('#' * 80) print('Results for ROAR.') # Print the final ensemble constructed by auto-sklearn via ROAR. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. @@ -129,7 +130,7 @@ def get_random_search_object_callback( print('Results for random search.') # Print the final ensemble constructed by auto-sklearn via random search. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. diff --git a/examples/60_search/example_sequential.py b/examples/60_search/example_sequential.py index b991802470..fad088396d 100644 --- a/examples/60_search/example_sequential.py +++ b/examples/60_search/example_sequential.py @@ -8,6 +8,7 @@ sequentially. The example below shows how to first fit the models and build the ensembles afterwards. """ +from pprint import pprint import sklearn.model_selection import sklearn.datasets @@ -48,7 +49,7 @@ # Print the final ensemble constructed by auto-sklearn # ==================================================== -print(automl.show_models()) +pprint(automl.show_models(), indent=4) ############################################################################ # Get the Score of the final ensemble diff --git a/examples/60_search/example_successive_halving.py b/examples/60_search/example_successive_halving.py index 4f95296aef..fdb29da6e0 100644 --- a/examples/60_search/example_successive_halving.py +++ b/examples/60_search/example_successive_halving.py @@ -14,7 +14,7 @@ To get the BOHB algorithm, simply import Hyperband and use it as the intensification strategy. """ # noqa (links are too long) - +from pprint import pprint import sklearn.model_selection import sklearn.datasets @@ -110,7 +110,7 @@ def get_smac_object( ) automl.fit(X_train, y_train, dataset_name='breast_cancer') -print(automl.show_models()) +pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. @@ -143,7 +143,7 @@ def get_smac_object( automl.fit(X_train, y_train, dataset_name='breast_cancer') # Print the final ensemble constructed by auto-sklearn. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) automl.refit(X_train, y_train) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of @@ -177,7 +177,7 @@ def get_smac_object( automl.fit(X_train, y_train, dataset_name='breast_cancer') # Print the final ensemble constructed by auto-sklearn. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) automl.refit(X_train, y_train) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of @@ -208,7 +208,7 @@ def get_smac_object( automl.fit(X_train, y_train, dataset_name='breast_cancer') # Print the final ensemble constructed by auto-sklearn. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. @@ -245,7 +245,7 @@ def get_smac_object( automl.fit(X_train, y_train, dataset_name='breast_cancer') # Print the final ensemble constructed by auto-sklearn. -print(automl.show_models()) +pprint(automl.show_models(), indent=4) predictions = automl.predict(X_test) # Print statistics about the auto-sklearn run such as number of # iterations, number of models failed with a time out. diff --git a/examples/80_extending/example_extending_classification.py b/examples/80_extending/example_extending_classification.py index 3c6c880a0c..b6132f4c18 100644 --- a/examples/80_extending/example_extending_classification.py +++ b/examples/80_extending/example_extending_classification.py @@ -6,6 +6,7 @@ The following example demonstrates how to create a new classification component for using in auto-sklearn. """ +from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ @@ -149,4 +150,4 @@ def get_hyperparameter_search_space(dataset_properties=None): y_pred = clf.predict(X_test) print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test)) -print(clf.show_models()) +pprint(clf.show_models(), indent=4) diff --git a/examples/80_extending/example_extending_data_preprocessor.py b/examples/80_extending/example_extending_data_preprocessor.py index 6a92fa2bc9..7fdd72e971 100644 --- a/examples/80_extending/example_extending_data_preprocessor.py +++ b/examples/80_extending/example_extending_data_preprocessor.py @@ -5,6 +5,7 @@ The following example demonstrates how to turn off data preprocessing step in auto-skearn. """ +from pprint import pprint import autosklearn.classification import autosklearn.pipeline.components.data_preprocessing @@ -89,4 +90,4 @@ def get_hyperparameter_search_space(dataset_properties=None): y_pred = clf.predict(X_test) print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test)) -print(clf.show_models()) +pprint(clf.show_models(), indent=4) diff --git a/examples/80_extending/example_extending_preprocessor.py b/examples/80_extending/example_extending_preprocessor.py index a67528007d..9ac93a45b3 100644 --- a/examples/80_extending/example_extending_preprocessor.py +++ b/examples/80_extending/example_extending_preprocessor.py @@ -7,6 +7,7 @@ discriminant analysis (LDA) algorithm from sklearn and use it as a preprocessor in auto-sklearn. """ +from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter @@ -130,4 +131,4 @@ def get_hyperparameter_search_space(dataset_properties=None): y_pred = clf.predict(X_test) print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test)) -print(clf.show_models()) +pprint(clf.show_models(), indent=4) diff --git a/examples/80_extending/example_extending_regression.py b/examples/80_extending/example_extending_regression.py index 7ee53cc975..3bdc008d4e 100644 --- a/examples/80_extending/example_extending_regression.py +++ b/examples/80_extending/example_extending_regression.py @@ -6,6 +6,7 @@ The following example demonstrates how to create a new regression component for using in auto-sklearn. """ +from pprint import pprint from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ @@ -137,4 +138,4 @@ def get_hyperparameter_search_space(dataset_properties=None): # ===================================== y_pred = reg.predict(X_test) print("r2 score: ", sklearn.metrics.r2_score(y_pred, y_test)) -print(reg.show_models()) +pprint(reg.show_models(), indent=4) diff --git a/requirements.txt b/requirements.txt index b499ec0d94..b8b77798f9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ setuptools typing_extensions +distro numpy>=1.9.0 scipy>=1.7.0 diff --git a/scripts/2015_nips_paper/run/score_ensemble.py b/scripts/2015_nips_paper/run/score_ensemble.py index 787c3b9174..3d10954d94 100644 --- a/scripts/2015_nips_paper/run/score_ensemble.py +++ b/scripts/2015_nips_paper/run/score_ensemble.py @@ -10,7 +10,7 @@ from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.metrics import balanced_accuracy -from autosklearn.util.backend import create +from autosklearn.automl_common.common.utils.backend import create def _load_file(f): @@ -102,9 +102,12 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) losses = [] top_models_at_step = dict() - backend = create(input_directory, input_directory + "_output", - delete_tmp_folder_after_terminate=False, - shared_mode=True) + backend = create( + temporary_directory=input_directory, + output_directory=input_directory + "_output", + delete_tmp_folder_after_terminate=False, + prefix="auto-sklearn" + ) valid_labels = backend.load_targets_ensemble() score = balanced_accuracy @@ -165,9 +168,11 @@ def main(input_directories, output_file, task_id, seed, ensemble_size, n_jobs=1) def evaluate(input_directory, validation_files, test_files, ensemble_size=50): - backend = create(input_directory, input_directory + "_output", - delete_tmp_folder_after_terminate=False, - shared_mode=True) + backend = create( + temporary_directory=input_directory, + output_directory=input_directory + "_output", + delete_tmp_folder_after_terminate=False, + ) valid_labels = backend.load_targets_ensemble() D = backend.load_datamanager() diff --git a/setup.py b/setup.py index a38fd20948..6107e60321 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,6 @@ "mypy", "pytest-xdist", "pytest-timeout", - "flaky", "openml", "pre-commit", "pytest-cov", @@ -41,7 +40,14 @@ "notebook", "seaborn", ], - "docs": ["sphinx", "sphinx-gallery", "sphinx_bootstrap_theme", "numpydoc"], + "docs": [ + "sphinx<4.3", + "sphinx-gallery", + "sphinx_bootstrap_theme", + "numpydoc", + "sphinx_toolbox", + "docutils==0.16" + ], } with open(os.path.join(HERE, 'autosklearn', '__version__.py')) as fh: diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/conftest.py b/test/conftest.py index 10d9f3607d..d3df7508cd 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -7,7 +7,7 @@ import psutil import pytest -from autosklearn.util.backend import create, Backend +from autosklearn.automl_common.common.utils.backend import create, Backend from autosklearn.automl import AutoML @@ -49,8 +49,9 @@ def backend(request): # Make sure the folders we wanna create do not already exist. backend = create( - tmp, - delete_tmp_folder_after_terminate=True, + temporary_directory=tmp, + output_directory=None, + prefix="auto-sklearn" ) def get_finalizer(tmp_dir): diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 34c3f58889..f021279dce 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -64,9 +64,9 @@ def test_fit(dask_client): metric=accuracy, dask_client=dask_client, ) - automl.fit( - X_train, Y_train, task=MULTICLASS_CLASSIFICATION - ) + + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + score = automl.score(X_test, Y_test) assert score > 0.8 assert count_succeses(automl.cv_results_) > 0 @@ -109,9 +109,9 @@ def get_roar_object_callback( metric=accuracy, dask_client=dask_client_single_worker, ) - automl.fit( - X_train, Y_train, task=MULTICLASS_CLASSIFICATION, - ) + + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + score = automl.score(X_test, Y_test) assert score > 0.8 assert count_succeses(automl.cv_results_) > 0 @@ -224,8 +224,7 @@ def test_delete_non_candidate_models(dask_client): max_models_on_disc=3, ) - automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, - X_test=X, y_test=Y) + automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y) # Assert at least one model file has been deleted and that there were no # deletion errors @@ -271,7 +270,9 @@ def test_binary_score_and_include(dask_client): metric=accuracy, dask_client=dask_client, ) + automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION) + assert automl._task == BINARY_CLASSIFICATION # TODO, the assumption from above is not really tested here @@ -294,6 +295,7 @@ def test_automl_outputs(dask_client): dask_client=dask_client, delete_tmp_folder_after_terminate=False, ) + auto.fit( X=X_train, y=Y_train, @@ -302,6 +304,7 @@ def test_automl_outputs(dask_client): dataset_name=name, task=MULTICLASS_CLASSIFICATION, ) + data_manager_file = os.path.join( auto._backend.temporary_directory, '.auto-sklearn', @@ -434,11 +437,16 @@ def test_do_dummy_prediction(dask_client, datasets): # Ensure that the dummy predictions are not in the current working # directory, but in the temporary directory. - assert not os.path.exists(os.path.join(os.getcwd(), '.auto-sklearn')) - assert os.path.exists(os.path.join( - auto._backend.temporary_directory, '.auto-sklearn', 'runs', '1_1_0.0', - 'predictions_ensemble_1_1_0.0.npy') + unexpected_directory = os.path.join(os.getcwd(), '.auto-sklearn') + expected_directory = os.path.join( + auto._backend.temporary_directory, + '.auto-sklearn', + 'runs', + '1_1_0.0', + 'predictions_ensemble_1_1_0.0.npy' ) + assert not os.path.exists(unexpected_directory) + assert os.path.exists(expected_directory) auto._clean_logger() @@ -619,9 +627,8 @@ def test_load_best_individual_model(metric, dask_client): # We cannot easily mock a function sent to dask # so for this test we create the whole set of models/ensembles # but prevent it to be loaded - automl.fit( - X_train, Y_train, task=MULTICLASS_CLASSIFICATION, - ) + automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION) + automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None) # A memory error occurs in the ensemble construction diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index f940550ffa..4de0f767aa 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -28,6 +28,7 @@ from sklearn.base import ClassifierMixin, RegressorMixin from sklearn.base import is_classifier from smac.tae import StatusType +from dask.distributed import Client from autosklearn.data.validation import InputValidator import autosklearn.pipeline.util as putil @@ -79,6 +80,7 @@ def __call__(self, *args, **kwargs): get_smac_object_callback=get_smac_object_wrapper_instance, max_models_on_disc=None, ) + automl.fit(X_train, Y_train) # Test that the argument is correctly passed to SMAC @@ -272,6 +274,7 @@ def test_performance_over_time_no_ensemble(tmp_dir): seed=1, initial_configurations_via_metalearning=0, ensemble_size=0,) + cls.fit(X_train, Y_train, X_test, Y_test) performance_over_time = cls.performance_over_time_ @@ -297,6 +300,7 @@ def test_cv_results(tmp_dir): original_params = copy.deepcopy(params) cls.fit(X_train, Y_train) + cv_results = cls.cv_results_ assert isinstance(cv_results, dict), type(cv_results) assert isinstance(cv_results['mean_test_score'], np.ndarray), type( @@ -382,6 +386,7 @@ def test_leaderboard( tmp_folder=tmp_dir, seed=1 ) + model.fit(X_train, Y_train) for params in params_generator: @@ -465,6 +470,157 @@ def exclude(lst, s): assert all(leaderboard['ensemble_weight'] > 0) +@pytest.mark.parametrize('estimator', [AutoSklearnRegressor]) +@pytest.mark.parametrize('resampling_strategy', ['holdout']) +@pytest.mark.parametrize('X', [ + np.asarray([[1.0, 1.0, 1.0]] * 25 + [[2.0, 2.0, 2.0]] * 25 + + [[3.0, 3.0, 3.0]] * 25 + [[4.0, 4.0, 4.0]] * 25) +]) +@pytest.mark.parametrize('y', [ + np.asarray([1.0] * 25 + [2.0] * 25 + [3.0] * 25 + [4.0] * 25) +]) +def test_show_models_with_holdout( + tmp_dir: str, + dask_client: Client, + estimator: AutoSklearnEstimator, + resampling_strategy: str, + X: np.ndarray, + y: np.ndarray +) -> None: + """ + Parameters + ---------- + tmp_dir: str + The temporary directory to use for this test + + dask_client: dask.distributed.Client + The dask client to use for this test + + estimator: AutoSklearnEstimator + The estimator to train + + resampling_strategy: str + The resampling strategy to use + + X: np.ndarray + The X data to use for this estimator + + y: np.ndarray + The targets to use for this estimator + + Expects + ------- + * Expects all the model dictionaries to have ``model_keys`` + * Expects all models to have an auto-sklearn wrapped model ``regressor`` + * Expects all models to have a sklearn wrapped model ``sklearn_regressor`` + * Expects no model to have any ``None`` value + """ + + automl = estimator( + time_left_for_this_task=60, + per_run_time_limit=5, + tmp_folder=tmp_dir, + resampling_strategy=resampling_strategy, + dask_client=dask_client + ) + automl.fit(X, y) + + models = automl.show_models().values() + + model_keys = set([ + 'model_id', 'rank', 'cost', 'ensemble_weight', + 'data_preprocessor', 'feature_preprocessor', + 'regressor', 'sklearn_regressor' + ]) + + assert all([model_keys == set(model.keys()) for model in models]) + assert all([model['regressor'] for model in models]) + assert all([model['sklearn_regressor'] for model in models]) + assert not any([None in model.values() for model in models]) + + +@pytest.mark.parametrize('estimator', [AutoSklearnClassifier]) +@pytest.mark.parametrize('resampling_strategy', ['cv']) +@pytest.mark.parametrize('X', [ + np.asarray([[1.0, 1.0, 1.0]] * 50 + [[2.0, 2.0, 2.0]] * 50) +]) +@pytest.mark.parametrize('y', [ + np.asarray([1] * 50 + [2] * 50) +]) +def test_show_models_with_cv( + tmp_dir: str, + dask_client: Client, + estimator: AutoSklearnEstimator, + resampling_strategy: str, + X: np.ndarray, + y: np.ndarray +) -> None: + """ + Parameters + ---------- + tmp_dir: str + The temporary directory to use for this test + + dask_client: dask.distributed.Client + The dask client to use for this test + + estimator: AutoSklearnEstimator + The estimator to train + + resampling_strategy: str + The resampling strategy to use + + X: np.ndarray + The X data to use for this estimator + + y: np.ndarray + The targets to use for this estimator + + Expects + ------- + * Expects all the model dictionaries to have ``model_keys`` + * Expects no model to have any ``None`` value + * Expects all the estimators in a model to have ``estimator_keys`` + * Expects all model estimators to have an auto-sklearn wrapped model ``classifier`` + * Expects all model estimators to have a sklearn wrapped model ``sklearn_classifier`` + * Expects no estimator to have ``None`` value + """ + + automl = estimator( + time_left_for_this_task=120, + per_run_time_limit=5, + tmp_folder=tmp_dir, + resampling_strategy=resampling_strategy, + dask_client=dask_client + ) + automl.fit(X, y) + + models = automl.show_models().values() + + model_keys = set([ + 'model_id', 'rank', + 'cost', 'ensemble_weight', + 'voting_model', 'estimators' + ]) + + estimator_keys = set([ + 'data_preprocessor', 'balancing', + 'feature_preprocessor', 'classifier', + 'sklearn_classifier' + ]) + + assert all([model_keys == set(model.keys()) for model in models]) + assert not any([None in model.values() for model in models]) + assert all([estimator_keys == set(estimator.keys()) + for model in models for estimator in model['estimators']]) + assert all([estimator['classifier'] + for model in models for estimator in model['estimators']]) + assert all([estimator['sklearn_classifier'] + for model in models for estimator in model['estimators']]) + assert not any([None in estimator.values() + for model in models for estimator in model['estimators']]) + + @unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl') def test_fit_n_jobs_negative(build_automl_patch): n_cores = cpu_count() @@ -540,6 +696,7 @@ def test_can_pickle_classifier(tmp_dir, dask_client): tmp_folder=tmp_dir, dask_client=dask_client, ) + automl.fit(X_train, Y_train) initial_predictions = automl.predict(X_test) @@ -637,7 +794,7 @@ def test_classification_pandas_support(tmp_dir, dask_client): ) # Drop NAN!! - X = X.dropna('columns') + X = X.dropna(axis='columns') # This test only make sense if input is dataframe assert isinstance(X, pd.DataFrame) @@ -765,12 +922,14 @@ def test_autosklearn_classification_methods_returns_self(dask_client): exclude={'feature_preprocessor': ['fast_ica']}) automl_fitted = automl.fit(X_train, y_train) + assert automl is automl_fitted automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + assert automl is automl_refitted @@ -801,12 +960,14 @@ def test_autosklearn2_classification_methods_returns_self(dask_client): dask_client=dask_client) automl_fitted = automl.fit(X_train, y_train) + assert automl is automl_fitted automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + assert automl is automl_refitted predictions = automl_fitted.predict(X_test) @@ -824,12 +985,14 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client): dask_client=dask_client) automl_fitted = automl.fit(X_train, y_train) + assert automl is automl_fitted automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) assert automl is automl_ensemble_fitted automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + assert automl is automl_refitted predictions = automl_fitted.predict(X_test) @@ -933,10 +1096,15 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_ X_test=X_test, y_test=y_test, ).get_default_configuration() - pipeline, run_info, run_value = automl.fit_pipeline(X=X_train, y=y_train, config=config, - X_test=X_test, y_test=y_test, - disable_file_output=disable_file_output, - resampling_strategy=resampling_strategy) + pipeline, run_info, run_value = automl.fit_pipeline( + X=X_train, + y=y_train, + config=config, + X_test=X_test, + y_test=y_test, + disable_file_output=disable_file_output, + resampling_strategy=resampling_strategy + ) assert isinstance(run_info.config, Configuration) assert run_info.cutoff == 30 @@ -1090,11 +1258,14 @@ def test_autosklearn_anneal(as_frame): if as_frame: # Let autosklearn calculate the feat types automl_fitted = automl.fit(X, y) + else: X_, y_ = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=True) feat_type = ['categorical' if X_[col].dtype.name == 'category' else 'numerical' for col in X_.columns] + automl_fitted = automl.fit(X, y, feat_type=feat_type) + assert automl is automl_fitted automl_ensemble_fitted = automl.fit_ensemble(y, ensemble_size=5) diff --git a/test/test_data/test_target_validator.py b/test/test_data/test_target_validator.py index bef309468d..e864c400e5 100644 --- a/test/test_data/test_target_validator.py +++ b/test/test_data/test_target_validator.py @@ -67,7 +67,7 @@ def input_data_targettest(request): y = y.dropna() y.replace('FALSE', 0, inplace=True) y.replace('TRUE', 1, inplace=True) - y = y.astype(np.int) + y = y.astype(int) return y elif 'sparse' in request.param: # We expect the names to be of the type sparse_csc_nonan diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py index f0f68044e2..b98021c7bd 100644 --- a/test/test_ensemble_builder/ensemble_utils.py +++ b/test/test_ensemble_builder/ensemble_utils.py @@ -5,10 +5,10 @@ import numpy as np +from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import AbstractEnsemble + from autosklearn.metrics import make_scorer -from autosklearn.ensemble_builder import ( - EnsembleBuilder, AbstractEnsemble -) +from autosklearn.ensemble_builder import EnsembleBuilder def scorer_function(a, b): diff --git a/test/test_evaluation/evaluation_util.py b/test/test_evaluation/evaluation_util.py index db48703042..e8ba4edf07 100644 --- a/test/test_evaluation/evaluation_util.py +++ b/test/test_evaluation/evaluation_util.py @@ -9,8 +9,8 @@ from sklearn import preprocessing import sklearn.model_selection +from autosklearn.automl_common.common.utils.backend import Backend -from autosklearn.util.backend import Backend from autosklearn.constants import \ MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION, REGRESSION from autosklearn.util.data import convert_to_bin diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index 7c3e31b603..f51820221b 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -10,10 +10,11 @@ import numpy as np import sklearn.dummy +from autosklearn.automl_common.common.utils.backend import Backend, BackendContext + from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator from autosklearn.pipeline.components.base import _addons from autosklearn.metrics import accuracy -from autosklearn.util.backend import Backend, BackendContext from smac.tae import StatusType this_directory = os.path.dirname(__file__) @@ -252,12 +253,15 @@ def test_file_output(self): context = BackendContext( temporary_directory=os.path.join(self.working_directory, 'tmp'), + output_directory=os.path.join(self.working_directory, 'tmp_output'), delete_tmp_folder_after_terminate=True, + delete_output_folder_after_terminate=True, + prefix="auto-sklearn" ) with unittest.mock.patch.object(Backend, 'load_datamanager') as load_datamanager_mock: load_datamanager_mock.return_value = get_multiclass_classification_datamanager() - backend = Backend(context) + backend = Backend(context, prefix="auto-sklearn") ae = AbstractEvaluator( backend=backend, @@ -294,11 +298,14 @@ def test_add_additional_components(self): context = BackendContext( temporary_directory=os.path.join(self.working_directory, 'tmp'), + output_directory=os.path.join(self.working_directory, 'tmp_output'), delete_tmp_folder_after_terminate=True, + delete_output_folder_after_terminate=True, + prefix="auto-sklearn" ) with unittest.mock.patch.object(Backend, 'load_datamanager') as load_datamanager_mock: load_datamanager_mock.return_value = get_multiclass_classification_datamanager() - backend = Backend(context) + backend = Backend(context, prefix="auto-sklearn") with unittest.mock.patch.object(_addons['classification'], 'add_component') as _: diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index d09ec8504a..93ea0c2265 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -13,12 +13,13 @@ import numpy as np from smac.tae import StatusType +from autosklearn.automl_common.common.utils.backend import Backend + from autosklearn.constants import MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION, \ MULTICLASS_CLASSIFICATION, REGRESSION from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t from autosklearn.evaluation.util import read_queue from autosklearn.util.pipeline import get_configuration_space -from autosklearn.util.backend import Backend from autosklearn.metrics import accuracy, r2, f1_macro this_directory = os.path.dirname(__file__) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 723abb0d41..28bddcdb09 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -19,12 +19,13 @@ import sklearn.model_selection from smac.tae import StatusType, TAEAbortException +from autosklearn.automl_common.common.utils import backend + import autosklearn.evaluation.splitter from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.evaluation.util import read_queue from autosklearn.evaluation.train_evaluator import TrainEvaluator, \ eval_holdout, eval_iterative_holdout, eval_cv, eval_partial_cv, subsample_indices -from autosklearn.util import backend from autosklearn.util.pipeline import get_configuration_space from autosklearn.constants import BINARY_CLASSIFICATION, \ MULTILABEL_CLASSIFICATION,\ @@ -92,7 +93,11 @@ def test_holdout(self, pipeline_mock): pipeline_mock.get_current_iter.return_value = 1 configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -160,7 +165,11 @@ def configuration_fully_fitted(self): pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -259,7 +268,11 @@ def configuration_fully_fitted(self): pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -331,7 +344,11 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): pipeline_mock.get_additional_run_info.return_value = None configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -375,7 +392,11 @@ def test_cv(self, pipeline_mock): pipeline_mock.get_additional_run_info.return_value = None configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -431,7 +452,11 @@ def test_partial_cv(self, pipeline_mock): D.name = 'test' configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -493,7 +518,11 @@ def configuration_fully_fitted(self): pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512) configuration = unittest.mock.Mock(spec=Configuration) - backend_api = backend.create(self.tmp_dir) + backend_api = backend.create( + temporary_directory=self.tmp_dir, + output_directory=None, + prefix="auto-sklearn" + ) backend_api.load_datamanager = lambda: D queue_ = multiprocessing.Queue() @@ -646,7 +675,7 @@ def test_file_output(self, loss_mock, model_mock): ) ) - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_classification(self, mock, backend_mock): @@ -698,7 +727,7 @@ def test_subsample_indices_classification(self, mock, backend_mock): 'classes = 2', subsample_indices, train_indices, 0.9999, evaluator.task_type, evaluator.Y_train) - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_regression(self, mock, backend_mock): @@ -771,7 +800,7 @@ def test_predict_proba_binary_classification(self, mock): @unittest.mock.patch.object(TrainEvaluator, 'file_output') @unittest.mock.patch.object(TrainEvaluator, '_partial_fit_and_predict_standard') - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_fit_predict_and_loss_standard_additional_run_info( self, mock, backend_mock, _partial_fit_and_predict_mock, @@ -864,7 +893,7 @@ def __call__(self, *args, **kwargs): @unittest.mock.patch.object(TrainEvaluator, '_loss') @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_fit_predict_and_loss_iterative_additional_run_info( self, mock, backend_mock, finish_up_mock, loss_mock, @@ -913,7 +942,7 @@ def __call__(self): @unittest.mock.patch.object(TrainEvaluator, '_loss') @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( self, mock, backend_mock, finish_up_mock, loss_mock, @@ -952,7 +981,7 @@ def test_fit_predict_and_loss_iterative_noniterativemodel_additional_run_info( @unittest.mock.patch.object(TrainEvaluator, '_loss') @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_fit_predict_and_loss_budget_additional_run_info( self, mock, backend_mock, finish_up_mock, loss_mock, @@ -1003,7 +1032,7 @@ def __call__(self): @unittest.mock.patch.object(TrainEvaluator, '_loss') @unittest.mock.patch.object(TrainEvaluator, 'finish_up') - @unittest.mock.patch('autosklearn.util.backend.Backend') + @unittest.mock.patch('autosklearn.automl_common.common.utils.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_fit_predict_and_loss_budget_2_additional_run_info( self, mock, backend_mock, finish_up_mock, loss_mock, diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base.py b/test/test_metalearning/pyMetaLearn/test_meta_base.py index ffc2b3b593..b1ac39ee2a 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_base.py @@ -17,8 +17,8 @@ def setUp(self): data_dir = os.path.join(data_dir, 'test_meta_base_data') os.chdir(data_dir) - cs = autosklearn.pipeline.classification.SimpleClassificationPipeline()\ - .get_hyperparameter_search_space() + pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline() + cs = pipeline.get_hyperparameter_search_space() self.logger = logging.getLogger() self.base = MetaBase(cs, data_dir, logger=self.logger) diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 99a641df7d..3239184469 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -158,7 +158,7 @@ def test_missing_values(sparse_data): X, y, logging.getLogger('Meta'), categorical) assert sparse.issparse(mf.value) assert mf.value.shape == X.shape - assert mf.value.dtype == np.bool + assert mf.value.dtype == bool assert 0 == np.sum(mf.value.data) diff --git a/test/test_metalearning/pyMetaLearn/test_metalearner.py b/test/test_metalearning/pyMetaLearn/test_metalearner.py index 8780e4270f..58f2ce800a 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearner.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearner.py @@ -23,8 +23,8 @@ def setUp(self): data_dir = os.path.join(data_dir, 'test_meta_base_data') os.chdir(data_dir) - self.cs = autosklearn.pipeline.classification\ - .SimpleClassificationPipeline().get_hyperparameter_search_space() + pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline() + self.cs = pipeline.get_hyperparameter_search_space() self.logger = logging.getLogger() meta_base = MetaBase(self.cs, data_dir, logger=self.logger) diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py index ea00da9275..3c6ff73c2b 100644 --- a/test/test_metric/test_metrics.py +++ b/test/test_metric/test_metrics.py @@ -1,4 +1,5 @@ import unittest +import warnings import pytest @@ -381,6 +382,17 @@ def test_classification_binary(self): self.assertLess(current_score, previous_score) def test_classification_multiclass(self): + # The last check in this test has a mismatch between the number of + # labels predicted in y_pred and the number of labels in y_true. + # This triggers several warnings but we are aware. + # + # TODO convert to pytest with fixture + # + # This test should be parameterized so we can identify which metrics + # cause which warning specifically and rectify if needed. + ignored_warnings = [ + (UserWarning, 'y_pred contains classes not in y_true') + ] for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): # Skip functions not applicable for multiclass classification. @@ -388,27 +400,51 @@ def test_classification_multiclass(self): 'precision', 'recall', 'f1', 'precision_samples', 'recall_samples', 'f1_samples']: continue - y_true = np.array([0.0, 0.0, 1.0, 1.0, 2.0]) - y_pred = np.array([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + + y_true = np.array( + [0.0, 0.0, 1.0, 1.0, 2.0] + ) + + y_pred = np.array([ + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0] + ]) previous_score = scorer._optimum current_score = scorer(y_true, y_pred) self.assertAlmostEqual(current_score, previous_score) - y_pred = np.array([[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], - [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + y_pred = np.array([ + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ]) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0]]) + y_pred = np.array([ + [0.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0] + ]) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) - y_pred = np.array([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0], - [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) + y_pred = np.array([ + [0.0, 0.0, 1.0], + [0.0, 0.0, 1.0], + [1.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0] + ]) previous_score = current_score current_score = scorer(y_true, y_pred) self.assertLess(current_score, previous_score) @@ -419,8 +455,15 @@ def test_classification_multiclass(self): [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] ) - score = scorer(y_true, y_pred) - self.assertTrue(np.isfinite(score)) + + with warnings.catch_warnings(): + for category, message in ignored_warnings: + warnings.filterwarnings( + 'ignore', category=category, message=message + ) + + score = scorer(y_true, y_pred) + self.assertTrue(np.isfinite(score)) def test_classification_multilabel(self): diff --git a/test/test_pipeline/components/classification/test_base.py b/test/test_pipeline/components/classification/test_base.py index e6f2bc1393..4fc381af56 100644 --- a/test/test_pipeline/components/classification/test_base.py +++ b/test/test_pipeline/components/classification/test_base.py @@ -9,6 +9,8 @@ import sklearn.metrics import numpy as np +from test.test_pipeline.ignored_warnings import ignore_warnings, classifier_warnings + class BaseClassificationComponentTest(unittest.TestCase): # Magic command to not run tests on base class @@ -172,16 +174,18 @@ def test_default_digits_multilabel(self): if not self.module.get_properties()["handles_multilabel"]: return - for i in range(2): - predictions, targets, _ = \ - _test_classifier(classifier=self.module, - dataset='digits', - make_multilabel=True) - self.assertAlmostEqual(self.res["default_digits_multilabel"], - sklearn.metrics.precision_score( - targets, predictions, average='macro'), - places=self.res.get( - "default_digits_multilabel_places", 7)) + for _ in range(2): + predictions, targets, _ = _test_classifier( + classifier=self.module, dataset='digits', make_multilabel=True + ) + + score = sklearn.metrics.precision_score( + targets, predictions, average='macro', zero_division=0 + ) + self.assertAlmostEqual( + self.res["default_digits_multilabel"], score, + places=self.res.get("default_digits_multilabel_places", 7) + ) def test_default_digits_multilabel_predict_proba(self): @@ -272,7 +276,8 @@ def is_unset_param_raw_predictions_val_error(err): + " assignment" in err.args[0]) try: - model.fit(X.copy(), y.copy()) + with ignore_warnings(classifier_warnings): + model.fit(X.copy(), y.copy()) except ValueError as e: if is_AdaBoostClassifier_error(e) or is_QDA_error(e): return None diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 56a3dae3b1..268a8ea542 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -108,9 +108,7 @@ def test_weighting_effect(self): default = cs.get_default_configuration() default._values['balancing:strategy'] = strategy - classifier = SimpleClassificationPipeline( - config=default, **model_args - ) + classifier = SimpleClassificationPipeline(config=default, **model_args) classifier.fit(X_train, Y_train) predictions1 = classifier.predict(X_test) @@ -126,9 +124,7 @@ def test_weighting_effect(self): X_test = data_[0][100:] Y_test = data_[1][100:] - classifier = SimpleClassificationPipeline( - config=default, **model_args - ) + classifier = SimpleClassificationPipeline(config=default, **model_args) Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) @@ -157,8 +153,7 @@ def test_weighting_effect(self): include = {'classifier': ['sgd'], 'feature_preprocessor': [name]} - classifier = SimpleClassificationPipeline( - random_state=1, include=include) + classifier = SimpleClassificationPipeline(random_state=1, include=include) cs = classifier.get_hyperparameter_search_space() default = cs.get_default_configuration() default._values['balancing:strategy'] = strategy @@ -177,8 +172,7 @@ def test_weighting_effect(self): Y_test = data_[1][100:] default._values['balancing:strategy'] = strategy - classifier = SimpleClassificationPipeline( - default, random_state=1, include=include) + classifier = SimpleClassificationPipeline(default, random_state=1, include=include) Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) predictions = classifier.predict(X_test) diff --git a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py index dffa763397..2767093179 100644 --- a/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py +++ b/test/test_pipeline/components/data_preprocessing/test_categorical_imputation.py @@ -34,15 +34,16 @@ def test_default_imputation(input_data_imputation, categorical): X = X.astype('str').astype('object') X[mask] = np.nan else: - imputation_value = 0 + imputation_value = min(np.unique(X)) - 1 + Y = CategoricalImputation().fit_transform(X.copy()) - assert ((np.argwhere(Y == imputation_value) == np.argwhere(mask)).all()) - assert ((np.argwhere(Y != imputation_value) == np.argwhere(np.logical_not(mask))).all()) + + assert np.array_equal(Y == imputation_value, mask) + assert np.array_equal(Y != imputation_value, ~mask) @pytest.mark.parametrize('format_type', ('numpy', 'pandas')) def test_nonzero_numerical_imputation(format_type): - # First try with an array with 0 as only valid category. The imputation should # happen with -1 X = np.full(fill_value=np.nan, shape=(10, 10)) @@ -69,8 +70,9 @@ def test_nonzero_numerical_imputation(format_type): @pytest.mark.parametrize('input_data_imputation', ('numpy'), indirect=True) def test_default_sparse(input_data_imputation): X, mask = input_data_imputation - X = sparse.csc_matrix(X) + X = sparse.csr_matrix(X) Y = CategoricalImputation().fit_transform(X) Y = Y.todense() - assert (np.argwhere(Y == 0) == np.argwhere(mask)).all() - assert (np.argwhere(Y != 0) == np.argwhere(np.logical_not(mask))).all() + + np.testing.assert_equal(Y == 0, mask) + np.testing.assert_equal(Y != 0, ~mask) diff --git a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py index 902ff1c9b3..dbffe26f51 100644 --- a/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py +++ b/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py @@ -2,6 +2,8 @@ import numpy as np from scipy import sparse +import pytest + from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical \ import CategoricalPreprocessingPipeline @@ -97,3 +99,11 @@ def test_transform_with_coalescence(self): # Consistency check: Y2t = CPPL.transform(X) np.testing.assert_array_equal(Y1t, Y2t) + + @pytest.mark.xfail(reason=( + "Encoding step does not support sparse matrices to convert negative labels to" + " positive ones as it does with non-sparse matrices" + )) + def test_transform_with_sparse_column_with_negative_labels(self): + X = sparse.csr_matrix([[0], [-1]]) + CategoricalPreprocessingPipeline().fit_transform(X) diff --git a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py index 839b0df947..19b1368a49 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py +++ b/test/test_pipeline/components/feature_preprocessing/test_kernel_pca.py @@ -1,7 +1,5 @@ import unittest -import pytest - from sklearn.linear_model import RidgeClassifier from autosklearn.pipeline.components.feature_preprocessing.kernel_pca import \ KernelPCA @@ -25,7 +23,6 @@ def test_default_configuration_sparse(self): self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) - @pytest.mark.flaky() def test_default_configuration_classify(self): for i in range(5): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', diff --git a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py index eb4b715ce9..19b56b6eac 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_liblinear.py +++ b/test/test_pipeline/components/feature_preprocessing/test_liblinear.py @@ -5,10 +5,15 @@ get_dataset import sklearn.metrics +from test.test_pipeline.ignored_warnings import ignore_warnings, feature_preprocessing_warnings + class LiblinearComponentTest(PreprocessingTestCase): + def test_default_configuration(self): - transformation, original = _test_preprocessing(LibLinear_Preprocessor) + with ignore_warnings(feature_preprocessing_warnings): + transformation, original = _test_preprocessing(LibLinear_Preprocessor) + self.assertEqual(transformation.shape[0], original.shape[0]) self.assertFalse((transformation == 0).all()) @@ -23,7 +28,10 @@ def test_default_configuration_classify(self): for hp_name in default if default[ hp_name] is not None}) - preprocessor.fit(X_train, Y_train) + + with ignore_warnings(feature_preprocessing_warnings): + preprocessor.fit(X_train, Y_train) + X_train_trans = preprocessor.transform(X_train) X_test_trans = preprocessor.transform(X_test) @@ -35,6 +43,6 @@ def test_default_configuration_classify(self): self.assertAlmostEqual(accuracy, 0.8548876745598057, places=2) def test_preprocessing_dtype(self): - super(LiblinearComponentTest, - self)._test_preprocessing_dtype(LibLinear_Preprocessor, - test_sparse=False) + + with ignore_warnings(feature_preprocessing_warnings): + super()._test_preprocessing_dtype(LibLinear_Preprocessor, test_sparse=False) diff --git a/test/test_pipeline/components/regression/test_base.py b/test/test_pipeline/components/regression/test_base.py index 32bf956557..8ffc1d23fe 100644 --- a/test/test_pipeline/components/regression/test_base.py +++ b/test/test_pipeline/components/regression/test_base.py @@ -1,13 +1,20 @@ +from typing import Type, Container + import unittest +import pytest + import numpy as np import sklearn.metrics -from autosklearn.pipeline.util import _test_regressor, \ - _test_regressor_iterative_fit +from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit from autosklearn.pipeline.constants import SPARSE from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR +from autosklearn.pipeline.components.regression import _regressors, RegressorChoice + +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings + class BaseRegressionComponentTest(unittest.TestCase): @@ -27,35 +34,36 @@ def test_default_boston(self): return for _ in range(2): - predictions, targets, n_calls = _test_regressor( - dataset="boston", Regressor=self.module - ) - if "default_boston_le_ge" in self.res: - # Special treatment for Gaussian Process Regression - self.assertLessEqual( - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), - self.res["default_boston_le_ge"][0] - ) - self.assertGreaterEqual( - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), - self.res["default_boston_le_ge"][1] + with ignore_warnings(regressor_warnings): + predictions, targets, n_calls = _test_regressor( + dataset="boston", + Regressor=self.module ) + + score = sklearn.metrics.r2_score(y_true=targets, y_pred=predictions) + + # Special treatment for Gaussian Process Regression + if "default_boston_le_ge" in self.res: + upper, lower = self.res["default_boston_le_ge"] + assert lower <= score <= upper + else: - score = sklearn.metrics.r2_score(targets, predictions) fixture = self.res["default_boston"] + places = self.res.get("default_boston_places", 7) + if score < -1e10: - print(f"score = {score}, fixture = {fixture}") score = np.log(-score) fixture = np.log(-fixture) - self.assertAlmostEqual( - fixture, - score, - places=self.res.get("default_boston_places", 7), - ) - if self.res.get("boston_n_calls"): - self.assertEqual(self.res["boston_n_calls"], n_calls) + self.assertAlmostEqual(fixture, score, places) + + if "boston_n_calls" in self.res: + expected = self.res["boston_n_calls"] + if isinstance(expected, Container): + assert n_calls in expected + else: + assert n_calls == expected def test_default_boston_iterative_fit(self): @@ -66,28 +74,36 @@ def test_default_boston_iterative_fit(self): return for i in range(2): - predictions, targets, regressor = \ - _test_regressor_iterative_fit(dataset="boston", - Regressor=self.module) + with ignore_warnings(regressor_warnings): + predictions, targets, regressor = _test_regressor_iterative_fit( + dataset="boston", + Regressor=self.module + ) + score = sklearn.metrics.r2_score(targets, predictions) fixture = self.res["default_boston_iterative"] + places = self.res.get("default_boston_iterative_places", 7) if score < -1e10: print(f"score = {score}, fixture = {fixture}") score = np.log(-score) fixture = np.log(-fixture) - self.assertAlmostEqual( - fixture, - score, - places=self.res.get("default_boston_iterative_places", 7), - ) + self.assertAlmostEqual(fixture, score, places) if self.step_hyperparameter is not None: - self.assertEqual( - getattr(regressor.estimator, self.step_hyperparameter['name']), - self.res.get("boston_iterative_n_iter", self.step_hyperparameter['value']) - ) + param_name = self.step_hyperparameter['name'] + default = self.step_hyperparameter['value'] + + value = getattr(regressor.estimator, param_name) + expected = self.res.get("boston_iterative_n_iter", default) + + # To currently allow for MLPRegressor which is indeterministic, + # we can have multiple values + if isinstance(expected, Container): + assert value in expected + else: + assert value == expected def test_default_boston_iterative_sparse_fit(self): @@ -101,10 +117,12 @@ def test_default_boston_iterative_sparse_fit(self): return for i in range(2): - predictions, targets, _ = \ - _test_regressor_iterative_fit(dataset="boston", - Regressor=self.module, - sparse=True) + with ignore_warnings(regressor_warnings): + predictions, targets, _ = _test_regressor_iterative_fit( + dataset="boston", + Regressor=self.module, + sparse=True + ) self.assertAlmostEqual(self.res["default_boston_iterative_sparse"], sklearn.metrics.r2_score(targets, predictions), @@ -120,10 +138,13 @@ def test_default_boston_sparse(self): return for i in range(2): - predictions, targets, _ = \ - _test_regressor(dataset="boston", - Regressor=self.module, - sparse=True) + with ignore_warnings(regressor_warnings): + predictions, targets, _ = _test_regressor( + dataset="boston", + Regressor=self.module, + sparse=True + ) + self.assertAlmostEqual(self.res["default_boston_sparse"], sklearn.metrics.r2_score(targets, predictions), @@ -136,9 +157,11 @@ def test_default_diabetes(self): return for i in range(2): - predictions, targets, n_calls = \ - _test_regressor(dataset="diabetes", - Regressor=self.module) + with ignore_warnings(regressor_warnings): + predictions, targets, n_calls = _test_regressor( + dataset="diabetes", + Regressor=self.module + ) self.assertAlmostEqual(self.res["default_diabetes"], sklearn.metrics.r2_score(targets, @@ -158,9 +181,12 @@ def test_default_diabetes_iterative_fit(self): return for i in range(2): - predictions, targets, _ = \ - _test_regressor_iterative_fit(dataset="diabetes", - Regressor=self.module) + with ignore_warnings(regressor_warnings): + predictions, targets, _ = _test_regressor_iterative_fit( + dataset="diabetes", + Regressor=self.module + ) + self.assertAlmostEqual(self.res["default_diabetes_iterative"], sklearn.metrics.r2_score(targets, predictions), @@ -179,10 +205,13 @@ def test_default_diabetes_iterative_sparse_fit(self): return for i in range(2): - predictions, targets, regressor = \ - _test_regressor_iterative_fit(dataset="diabetes", - Regressor=self.module, - sparse=True) + with ignore_warnings(regressor_warnings): + predictions, targets, regressor = _test_regressor_iterative_fit( + dataset="diabetes", + Regressor=self.module, + sparse=True + ) + self.assertAlmostEqual(self.res["default_diabetes_iterative_sparse"], sklearn.metrics.r2_score(targets, predictions), @@ -204,10 +233,13 @@ def test_default_diabetes_sparse(self): return for i in range(2): - predictions, targets, _ = \ - _test_regressor(dataset="diabetes", - Regressor=self.module, - sparse=True) + with ignore_warnings(regressor_warnings): + predictions, targets, _ = _test_regressor( + dataset="diabetes", + Regressor=self.module, + sparse=True + ) + self.assertAlmostEqual(self.res["default_diabetes_sparse"], sklearn.metrics.r2_score(targets, predictions), @@ -257,12 +289,16 @@ def test_module_idempotent(self): # Get the parameters on the first and second fit with config params # Also compare their random state - params_first = regressor.fit(X.copy(), y.copy()).estimator.get_params() + with ignore_warnings(regressor_warnings): + params_first = regressor.fit(X.copy(), y.copy()).estimator.get_params() + if hasattr(regressor.estimator, 'random_state'): rs_1 = regressor.random_state rs_estimator_1 = regressor.estimator.random_state - params_second = regressor.fit(X.copy(), y.copy()).estimator.get_params() + with ignore_warnings(regressor_warnings): + params_second = regressor.fit(X.copy(), y.copy()).estimator.get_params() + if hasattr(regressor.estimator, 'random_state'): rs_2 = regressor.random_state rs_estimator_2 = regressor.estimator.random_state @@ -286,3 +322,138 @@ def test_module_idempotent(self): seed == random_state for random_state in [rs_1, rs_estimator_1, rs_2, rs_estimator_2] ]) + + +@pytest.mark.parametrize("regressor", _regressors.values()) +@pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) +@pytest.mark.parametrize("y", [np.array([1] * 20)]) +def test_fit_and_predict_with_1d_targets_as_1d( + regressor: Type[RegressorChoice], + X: np.ndarray, + y: np.ndarray +) -> None: + """Test that all pipelines work with 1d target types + + Parameters + ---------- + regressor: RegressorChoice + The regressor to test + + X: np.ndarray + The features + + y: np.ndarray + The 1d targets + + Expects + ------- + * Should be able to fit with 1d targets + * Should be able to predict with 1d targest + * Should have predictions with the same shape as y + """ + assert len(X) == len(y) + assert y.ndim == 1 + + config_space = regressor.get_hyperparameter_search_space() + default_config = config_space.get_default_configuration() + + model = regressor(random_state=0, **default_config) + + with ignore_warnings(regressor_warnings): + model.fit(X, y) + + predictions = model.predict(X) + + assert predictions.shape == y.shape + + +@pytest.mark.parametrize("regressor", _regressors.values()) +@pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) +@pytest.mark.parametrize("y", [np.array([[1]] * 20)]) +def test_fit_and_predict_with_1d_targets_as_2d( + regressor: Type[RegressorChoice], + X: np.ndarray, + y: np.ndarray +) -> None: + """Test that all pipelines work with 1d target types when they are wrapped as 2d + + Parameters + ---------- + regressor: RegressorChoice + The regressor to test + + X: np.ndarray + The features + + y: np.ndarray + The 1d targets wrapped as 2d + + Expects + ------- + * Should be able to fit with 1d targets wrapped in 2d + * Should be able to predict 1d targets wrapped in 2d + * Should return 1d predictions + * Should have predictions with the same length as the y + """ + assert len(X) == len(y) + assert y.ndim == 2 and y.shape[1] == 1 + + config_space = regressor.get_hyperparameter_search_space() + default_config = config_space.get_default_configuration() + + model = regressor(random_state=0, **default_config) + + with ignore_warnings(regressor_warnings): + model.fit(X, y) + + predictions = model.predict(X) + + assert predictions.ndim == 1 + assert len(predictions) == len(y) + + +@pytest.mark.parametrize("regressor", [ + regressor + for regressor in _regressors.values() + if regressor.get_properties()['handles_multilabel'] +]) +@pytest.mark.parametrize("X", [np.array([[1, 2, 3]] * 20)]) +@pytest.mark.parametrize("y", [np.array([[1, 1, 1]] * 20)]) +def test_fit_and_predict_with_2d_targets( + regressor: Type[RegressorChoice], + X: np.ndarray, + y: np.ndarray +) -> None: + """Test that all pipelines work with 2d target types + + Parameters + ---------- + regressor: RegressorChoice + The regressor to test + + X: np.ndarray + The features + + y: np.ndarray + The 2d targets + + Expects + ------- + * Should be able to fit with 2d targets + * Should be able to predict with 2d targets + * Should have predictions with the same shape as y + """ + assert len(X) == len(y) + assert y.ndim == 2 and y.shape[1] > 1 + + config_space = regressor.get_hyperparameter_search_space() + default_config = config_space.get_default_configuration() + + model = regressor(random_state=0, **default_config) + + with ignore_warnings(regressor_warnings): + model.fit(X, y) + + predictions = model.predict(X) + + assert predictions.shape == y.shape diff --git a/test/test_pipeline/components/regression/test_mlp.py b/test/test_pipeline/components/regression/test_mlp.py index e3843d2197..c003037c76 100644 --- a/test/test_pipeline/components/regression/test_mlp.py +++ b/test/test_pipeline/components/regression/test_mlp.py @@ -1,3 +1,5 @@ +from typing import Any, Dict + import sklearn.neural_network from autosklearn.pipeline.components.regression.mlp import MLPRegressor @@ -22,21 +24,36 @@ class MLPComponentTest(BaseRegressionComponentTest): # # These seem to have consistent CPU's so I'm unsure what the underlying reason # for this to randomly fail only sometimes on Github runners + # + # Edit: If changing, please tracke what values were failing + # + # Seems there is a consistently different values for boston so: + # * include two valuess for n_iter in 'boston_iterative_n_iter' + # known-values = [236, 331] + # + # * decreased places from 6 -> 5 in 'default_boston_{sparse,_iterative_sparse}' + # to check for for iterations and expanded the default places for checking + # know-values = [-0.10972947168054104, -0.10973142976866268] + # + # * decreased places from 3 -> 1 in 'default_boston_places' + # known-values = [0.29521793994422807, 0.2750079862455884] + # + # * Include two value for 'boston_n_calls' + # known-values = [8, 9] __test__ = True - __test__ = True - res = dict() + res: Dict[str, Any] = {} res["default_boston"] = 0.2750079862455884 - res["default_boston_places"] = 3 - res["boston_n_calls"] = 8 - res["boston_iterative_n_iter"] = 236 + res["default_boston_places"] = 1 + res["boston_n_calls"] = [8, 9] + res["boston_iterative_n_iter"] = [236, 331] res["default_boston_iterative"] = res["default_boston"] res["default_boston_iterative_places"] = 1 res["default_boston_sparse"] = -0.10972947168054104 - res["default_boston_sparse_places"] = 6 + res["default_boston_sparse_places"] = 5 res["default_boston_iterative_sparse"] = res["default_boston_sparse"] - res["default_boston_iterative_sparse_places"] = 6 + res["default_boston_iterative_sparse_places"] = res["default_boston_sparse_places"] res["default_diabetes"] = 0.35917389841850555 res["diabetes_n_calls"] = 9 res["diabetes_iterative_n_iter"] = 435 diff --git a/test/test_pipeline/ignored_warnings.py b/test/test_pipeline/ignored_warnings.py new file mode 100644 index 0000000000..5b941281f9 --- /dev/null +++ b/test/test_pipeline/ignored_warnings.py @@ -0,0 +1,117 @@ +from contextlib import contextmanager +from typing import List, Iterator, Tuple + +import warnings + +from sklearn.exceptions import ConvergenceWarning + + +regressor_warnings = [ + ( + UserWarning, ( # From QuantileTransformer + r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." + r" n_quantiles is set to n_samples\." + ) + ), + ( + ConvergenceWarning, ( # From GaussianProcesses + r"The optimal value found for dimension \d+ of parameter \w+ is close" + r" to the specified (upper|lower) bound .*(Increasing|Decreasing) the bound" + r" and calling fit again may find a better value." + ) + ), + ( + UserWarning, ( # From FastICA + r"n_components is too large: it will be set to \d+" + ) + ), + ( + ConvergenceWarning, ( # From SGD + r"Maximum number of iteration reached before convergence\. Consider increasing" + r" max_iter to improve the fit\." + ) + ), + ( + ConvergenceWarning, ( # From MLP + r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" + r" optimization hasn't converged yet\." + ) + ), +] + +classifier_warnings = [ + ( + UserWarning, ( # From QuantileTransformer + r"n_quantiles \(\d+\) is greater than the total number of samples \(\d+\)\." + r" n_quantiles is set to n_samples\." + ) + ), + ( + UserWarning, ( # From FastICA + r"n_components is too large: it will be set to \d+" + ) + + ), + ( + ConvergenceWarning, ( # From Liblinear + r"Liblinear failed to converge, increase the number of iterations\." + ) + ), + ( + ConvergenceWarning, ( # From SGD + r"Maximum number of iteration reached before convergence\. Consider increasing" + r" max_iter to improve the fit\." + ) + ), + ( + ConvergenceWarning, ( # From MLP + r"Stochastic Optimizer: Maximum iterations \(\d+\) reached and the" + r" optimization hasn't converged yet\." + ) + ), + ( + ConvergenceWarning, ( # From FastICA + r"FastICA did not converge\." + r" Consider increasing tolerance or the maximum number of iterations\." + ) + ), + ( + UserWarning, ( # From LDA (Linear Discriminant Analysis) + r"Variables are collinear" + ) + ), + ( + UserWarning, ( + r"Clustering metrics expects discrete values but received continuous values" + r" for label, and multiclass values for target" + ) + ) +] + +feature_preprocessing_warnings = [ + ( + ConvergenceWarning, ( # From liblinear + r"Liblinear failed to converge, increase the number of iterations." + ) + ) +] + +ignored_warnings = regressor_warnings + classifier_warnings + feature_preprocessing_warnings + + +@contextmanager +def ignore_warnings(to_ignore: List[Tuple[Exception, str]] = ignored_warnings) -> Iterator[None]: + """A context manager to ignore warnings + + >>> with ignore_warnings(classifier_warnings): + >>> ... + + Parameters + ---------- + to_ignore: List[Tuple[Exception, str]] = ignored_warnings + The list of warnings to ignore, defaults to all registered warnings + """ + with warnings.catch_warnings(): + for category, message in to_ignore: + warnings.filterwarnings('ignore', category=category, message=message) + yield diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index d5864f14cd..44caaecb9b 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -1,9 +1,10 @@ +from typing import Any, Dict, Union + import copy import itertools import os import resource import tempfile -import traceback import unittest import unittest.mock @@ -31,6 +32,8 @@ from autosklearn.pipeline.constants import \ DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS, SIGNED_DATA, INPUT +from test.test_pipeline.ignored_warnings import classifier_warnings, ignore_warnings + class DummyClassifier(AutoSklearnClassificationAlgorithm): @staticmethod @@ -102,6 +105,12 @@ class SimpleClassificationPipelineTest(unittest.TestCase): _multiprocess_can_split_ = True def test_io_dict(self): + """Test for the properties of classifier components + + Expects + ------- + * All required properties are stated in class `get_properties()` + """ classifiers = classification_components._classifiers for c in classifiers: if classifiers[c] == classification_components.ClassifierChoice: @@ -124,6 +133,13 @@ def test_io_dict(self): self.assertIn('handles_multilabel', props) def test_find_classifiers(self): + """Test that the classifier components can be found + + Expects + ------- + * At least two classifier components can be found + * They inherit from AutoSklearnClassificationAlgorithm + """ classifiers = classification_components._classifiers self.assertGreaterEqual(len(classifiers), 2) for key in classifiers: @@ -132,6 +148,13 @@ def test_find_classifiers(self): self.assertIn(AutoSklearnClassificationAlgorithm, classifiers[key].__bases__) def test_find_preprocessors(self): + """Test that preproccesor components can be found + + Expects + ------- + * At least 1 preprocessor component can be found + * The inherit from AutoSklearnPreprocessingAlgorithm + """ preprocessors = preprocessing_components._preprocessors self.assertGreaterEqual(len(preprocessors), 1) for key in preprocessors: @@ -140,54 +163,98 @@ def test_find_preprocessors(self): self.assertIn(AutoSklearnPreprocessingAlgorithm, preprocessors[key].__bases__) def test_default_configuration(self): - for i in range(2): - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') - auto = SimpleClassificationPipeline(random_state=1) + """Test that seeded SimpleClassificaitonPipeline returns good results on iris + + Expects + ------- + * The performance of configuration with fixed seed gets above 96% accuracy on iris + """ + X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + + auto = SimpleClassificationPipeline(random_state=1) + + with ignore_warnings(classifier_warnings): auto = auto.fit(X_train, Y_train) - predictions = auto.predict(X_test) - self.assertAlmostEqual(0.96, sklearn.metrics.accuracy_score(predictions, Y_test)) - auto.predict_proba(X_test) + + predictions = auto.predict(X_test) + + acc = sklearn.metrics.accuracy_score(predictions, Y_test) + self.assertAlmostEqual(0.96, acc) def test_default_configuration_multilabel(self): - for i in range(2): - classifier = SimpleClassificationPipeline( - random_state=1, - dataset_properties={'multilabel': True} - ) - cs = classifier.get_hyperparameter_search_space() - default = cs.get_default_configuration() - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris', - make_multilabel=True) - classifier.set_hyperparameters(default) + """Test that SimpleClassificationPipeline default config returns good results on + a multilabel version of iris. + + Expects + ------- + * The performance of a random configuratino gets above 96% on a multilabel + version of iris + """ + X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris', make_multilabel=True) + + classifier = SimpleClassificationPipeline( + dataset_properties={'multilabel': True}, + random_state=0 + ) + cs = classifier.get_hyperparameter_search_space() + + default = cs.get_default_configuration() + classifier.set_hyperparameters(default) + + with ignore_warnings(classifier_warnings): classifier = classifier.fit(X_train, Y_train) - predictions = classifier.predict(X_test) - self.assertAlmostEqual(0.96, - sklearn.metrics.accuracy_score(predictions, - Y_test)) - classifier.predict_proba(X_test) + + predictions = classifier.predict(X_test) + + acc = sklearn.metrics.accuracy_score(predictions, Y_test) + self.assertAlmostEqual(0.96, acc) def test_default_configuration_iterative_fit(self): + """Test that the SimpleClassificationPipeline default config for random forest + with no preprocessing can be iteratively fit on iris. + + Expects + ------- + * Random forest pipeline can be fit iteratively + * Test that its number of estimators is equal to the iteration count + """ + X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + classifier = SimpleClassificationPipeline( - random_state=1, include={ 'classifier': ['random_forest'], 'feature_preprocessor': ['no_preprocessing'] - } + }, + random_state=0 ) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') classifier.fit_transformer(X_train, Y_train) - for i in range(1, 11): - classifier.iterative_fit(X_train, Y_train) - self.assertEqual( - classifier.steps[-1][-1].choice.estimator.n_estimators, i - ) + + with ignore_warnings(classifier_warnings): + for i in range(1, 11): + classifier.iterative_fit(X_train, Y_train) + n_estimators = classifier.steps[-1][-1].choice.estimator.n_estimators + self.assertEqual(n_estimators, i) def test_repr(self): + """Test that the default pipeline can be converted to its representation and + converted back. + + Expects + ------- + * The the SimpleClassificationPipeline has a repr + * This repr can be evaluated back to an instance of SimpleClassificationPipeline + """ representation = repr(SimpleClassificationPipeline()) cls = eval(representation) self.assertIsInstance(cls, SimpleClassificationPipeline) def test_multilabel(self): + """Test non-seeded configurations for multi-label data + + Expects + ------- + * All configurations should fit, predict and predict_proba successfully + """ cache = Memory(location=tempfile.gettempdir()) cached_func = cache.cache( sklearn.datasets.make_multilabel_classification @@ -204,110 +271,179 @@ def test_multilabel(self): return_distributions=False, random_state=1 ) - X_train = X[:100, :] - Y_train = Y[:100, :] - X_test = X[101:, :] - Y_test = Y[101:, ] - data = {'X_train': X_train, 'Y_train': Y_train, - 'X_test': X_test, 'Y_test': Y_test} + data = { + 'X_train': X[:100, :], + 'Y_train': Y[:100, :], + 'X_test': X[101:, :], + 'Y_test': Y[101:, ] + } - dataset_properties = {'multilabel': True} - cs = SimpleClassificationPipeline(dataset_properties=dataset_properties).\ - get_hyperparameter_search_space() - self._test_configurations(configurations_space=cs, data=data) + pipeline = SimpleClassificationPipeline(dataset_properties={"multilabel": True}) + cs = pipeline.get_hyperparameter_search_space() + self._test_configurations(configurations_space=cs, dataset=data) def test_configurations(self): + """Tests a non-seeded random set of configurations with default dataset properties + + Expects + ------- + * All configurations should fit, predict and predict_proba successfully + """ cls = SimpleClassificationPipeline() cs = cls.get_hyperparameter_search_space() self._test_configurations(configurations_space=cs) def test_configurations_signed_data(self): + """Tests a non-seeded random set of configurations with signed data + + Expects + ------- + * All configurations should fit, predict and predict_proba successfully + """ dataset_properties = {'signed': True} - cs = SimpleClassificationPipeline(dataset_properties=dataset_properties)\ - .get_hyperparameter_search_space() - self._test_configurations(configurations_space=cs, - dataset_properties=dataset_properties) + cls = SimpleClassificationPipeline(dataset_properties=dataset_properties) + cs = cls.get_hyperparameter_search_space() + + self._test_configurations(configurations_space=cs, dataset_properties=dataset_properties) def test_configurations_sparse(self): - cs = SimpleClassificationPipeline(dataset_properties={'sparse': True}).\ - get_hyperparameter_search_space() + """Tests a non-seeded random set of configurations with sparse data + + Expects + ------- + * All configurations should fit, predict and predict_proba successfully + """ + pipeline = SimpleClassificationPipeline(dataset_properties={'sparse': True}) + cs = pipeline.get_hyperparameter_search_space() self._test_configurations(configurations_space=cs, make_sparse=True) def test_configurations_categorical_data(self): - cs = SimpleClassificationPipeline( + """Tests a non-seeded random set of configurations with sparse, mixed data + + Loads specific data from /components/data_preprocessing/dataset.pkl + + Expects + ------- + * All configurations should fit, predict and predict_proba successfully + """ + pipeline = SimpleClassificationPipeline( dataset_properties={'sparse': False}, - random_state=1, include={ 'feature_preprocessor': ['no_preprocessing'], 'classifier': ['sgd', 'adaboost'] } - ).get_hyperparameter_search_space() - - categorical = [True, True, True, False, False, True, True, True, - False, True, True, True, True, True, True, True, - True, True, True, True, True, True, True, True, True, - True, True, True, True, True, True, True, False, - False, False, True, True, True] - categorical = {i: 'categorical' if bool_cat else 'numerical' - for i, bool_cat in enumerate(categorical)} - this_directory = os.path.dirname(__file__) - X = np.loadtxt(os.path.join(this_directory, "components", - "data_preprocessing", "dataset.pkl")) + ) + + cs = pipeline.get_hyperparameter_search_space() + + categorical_columns = [ + True, True, True, False, False, True, True, True, False, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True, True, False, False, False, True, True, True + ] + categorical = { + i: 'categorical' if is_categorical else 'numerical' + for i, is_categorical in enumerate(categorical_columns) + } + + here = os.path.dirname(__file__) + dataset_path = os.path.join(here, "components", "data_preprocessing", "dataset.pkl") + + X = np.loadtxt(dataset_path) y = X[:, -1].copy() X = X[:, :-1] - X_train, X_test, Y_train, Y_test = \ - sklearn.model_selection.train_test_split(X, y) - data = {'X_train': X_train, 'Y_train': Y_train, - 'X_test': X_test, 'Y_test': Y_test} + X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, y) - init_params = { - 'data_preprocessor:feat_type': categorical - } + data = {'X_train': X_train, 'Y_train': Y_train, 'X_test': X_test, 'Y_test': Y_test} + + init_params = {'data_preprocessor:feat_type': categorical} - self._test_configurations(configurations_space=cs, make_sparse=True, - data=data, init_params=init_params) + self._test_configurations(configurations_space=cs, dataset=data, init_params=init_params) @unittest.mock.patch('autosklearn.pipeline.components.data_preprocessing' '.DataPreprocessorChoice.set_hyperparameters') def test_categorical_passed_to_one_hot_encoder(self, ohe_mock): + """Test that the feat_types arg is passed to the OneHotEncoder + + Expects + ------- + * Construction of SimpleClassificationPipeline to pass init_params correctly + to the OneHotEncoder + + * Setting the pipeline's hyperparameters after construction also correctly + sets the init params of the OneHotEncoder + """ # Mock the _check_init_params_honored as there is no object created, # _check_init_params_honored will fail as a datapreprocessor was never created with unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline' '._check_init_params_honored'): + + # Check through construction + feat_types = {0: 'categorical', 1: 'numerical'} + cls = SimpleClassificationPipeline( - init_params={'data_preprocessor:feat_type': {0: 'categorical', - 1: 'numerical'}} + init_params={'data_preprocessor:feat_type': feat_types} ) - self.assertEqual( - ohe_mock.call_args[1]['init_params'], - {'feat_type': {0: 'categorical', 1: 'numerical'}} - ) + init_args = ohe_mock.call_args[1]['init_params'] + self.assertEqual(init_args, {'feat_type': feat_types}) + + # Check through `set_hyperparameters` + feat_types = {0: 'categorical', 1: 'categorical', 2: 'numerical'} + default = cls.get_hyperparameter_search_space().get_default_configuration() cls.set_hyperparameters( configuration=default, - init_params={'data_preprocessor:feat_type': {0: 'categorical', - 1: 'categorical', - 2: 'numerical'}}, - ) - self.assertEqual( - ohe_mock.call_args[1]['init_params'], - {'feat_type': {0: 'categorical', 1: 'categorical', - 2: 'numerical'}} + init_params={'data_preprocessor:feat_type': feat_types}, ) - def _test_configurations(self, configurations_space, make_sparse=False, - data=None, init_params=None, - dataset_properties=None): + init_args = ohe_mock.call_args[1]['init_params'] + self.assertEqual(init_args, {'feat_type': feat_types}) + + def _test_configurations( + self, + configurations_space: ConfigurationSpace, + make_sparse: bool = False, + dataset: Union[str, Dict[str, Any]] = 'digits', + init_params: Dict[str, Any] = None, + dataset_properties: Dict[str, Any] = None, + n_samples: int = 10, + ): + """Tests a configuration space by taking multiple samples and fiting each + before calling predict and predict_proba. + + Parameters + ---------- + configurations_space: ConfigurationSpace + The configuration space to sample from + + make_sparse: bool = False + Whether to make the dataset sparse or not + + dataset: Union[str, Dict[str, Any]] = 'digits' + Either a dataset name or a dictionary as below. If given a str, it will + use `make_sparse` and add NaNs to the dataset. + + {'X_train': ..., 'Y_train': ..., 'X_test': ..., 'y_test': ...} + + init_params: Dict[str, Any] = None + A dictionary of initial parameters to give to the pipeline. + + dataset_properties: Dict[str, Any] + A dictionary of properties describing the dataset + + n_samples: int = 10 + How many configurations to sample + """ # Use a limit of ~3GiB limit = 3072 * 1024 * 1024 resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) - for i in range(10): + for i in range(n_samples): config = configurations_space.sample_configuration() config._populate_values() @@ -328,26 +464,29 @@ def _test_configurations(self, configurations_space, make_sparse=False, 'feature_preprocessor:feature_agglomeration:n_clusters': 2, 'classifier:gradient_boosting:max_leaf_nodes': 64} - for restrict_parameter in restrictions: - restrict_to = restrictions[restrict_parameter] - if restrict_parameter in config and \ - config[restrict_parameter] is not None: - config._values[restrict_parameter] = restrict_to + config._values.update({ + param: value + for param, value in restrictions.items() + if param in config and config[param] is not None + }) - if data is None: + if isinstance(dataset, str): X_train, Y_train, X_test, Y_test = get_dataset( - dataset='digits', make_sparse=make_sparse, add_NaNs=True) + dataset=dataset, + make_sparse=make_sparse, + add_NaNs=True + ) else: - X_train = data['X_train'].copy() - Y_train = data['Y_train'].copy() - X_test = data['X_test'].copy() - data['Y_test'].copy() + X_train = dataset['X_train'].copy() + Y_train = dataset['Y_train'].copy() + X_test = dataset['X_test'].copy() + dataset['Y_test'].copy() init_params_ = copy.deepcopy(init_params) + cls = SimpleClassificationPipeline( - random_state=1, dataset_properties=dataset_properties, - init_params=init_params_, + init_params=init_params_ ) cls.set_hyperparameters(config, init_params=init_params_) @@ -359,7 +498,8 @@ def _test_configurations(self, configurations_space, make_sparse=False, check_is_fitted(step) try: - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) # After fit, all components should be tagged as fitted # by sklearn. Check is fitted raises an exception if that @@ -368,19 +508,17 @@ def _test_configurations(self, configurations_space, make_sparse=False, for name, step in cls.named_steps.items(): check_is_fitted(step) except sklearn.exceptions.NotFittedError: - self.fail("config={} raised NotFittedError unexpectedly!".format( - config - )) + self.fail(f"config={config} raised NotFittedError unexpectedly!") cls.predict(X_test.copy()) cls.predict_proba(X_test) + except MemoryError: continue except np.linalg.LinAlgError: continue except ValueError as e: - if "Floating-point under-/overflow occurred at epoch" in \ - e.args[0]: + if "Floating-point under-/overflow occurred at epoch" in e.args[0]: continue elif "removed all features" in e.args[0]: continue @@ -396,9 +534,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, elif 'Internal work array size computation failed' in e.args[0]: continue else: - print(config) - print(traceback.format_exc()) + e.args += (f"config={config}",) raise e + except RuntimeWarning as e: if "invalid value encountered in sqrt" in e.args[0]: continue @@ -413,114 +551,158 @@ def _test_configurations(self, configurations_space, make_sparse=False, elif "invalid value encountered in multiply" in e.args[0]: continue else: - print(traceback.format_exc()) - print(config) + e.args += (f"config={config}",) raise e + except UserWarning as e: if "FastICA did not converge" in e.args[0]: continue else: - print(traceback.format_exc()) - print(config) + e.args += (f"config={config}",) raise e def test_get_hyperparameter_search_space(self): - cs = SimpleClassificationPipeline().get_hyperparameter_search_space() + """Test the configuration space returned by a SimpleClassificationPipeline + + Expects + ------- + * pipeline returns a configurations space + * 7 rescaling choices + * 16 classifier choices + * 13 features preprocessor choices + * 168 total hyperparameters + * (n_hyperparameters - 4) different conditionals for the pipeline + * 53 forbidden combinations + """ + pipeline = SimpleClassificationPipeline() + cs = pipeline.get_hyperparameter_search_space() self.assertIsInstance(cs, ConfigurationSpace) - conditions = cs.get_conditions() - forbiddens = cs.get_forbiddens() - param = 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__' - self.assertEqual(len(cs.get_hyperparameter(param).choices), 7) - self.assertEqual(len(cs.get_hyperparameter( - 'classifier:__choice__').choices), 16) - self.assertEqual(len(cs.get_hyperparameter( - 'feature_preprocessor:__choice__').choices), 13) + rescale_param = 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__' + n_choices = len(cs.get_hyperparameter(rescale_param).choices) + self.assertEqual(n_choices, 7) + + n_classifiers = len(cs.get_hyperparameter('classifier:__choice__').choices) + self.assertEqual(n_classifiers, 16) + + n_preprocessors = len(cs.get_hyperparameter('feature_preprocessor:__choice__').choices) + self.assertEqual(n_preprocessors, 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(168, len(hyperparameters)) + self.assertEqual(len(hyperparameters), 168) # for hp in sorted([str(h) for h in hyperparameters]): # print hp # The four components which are always active are classifier, # feature preprocessor, balancing and data preprocessing pipeline. + conditions = cs.get_conditions() self.assertEqual(len(hyperparameters) - 4, len(conditions)) + forbiddens = cs.get_forbiddens() self.assertEqual(len(forbiddens), 53) def test_get_hyperparameter_search_space_include_exclude_models(self): - cs = SimpleClassificationPipeline(include={'classifier': ['libsvm_svc']})\ - .get_hyperparameter_search_space() - self.assertEqual( - cs.get_hyperparameter('classifier:__choice__'), - CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']), - ) + """Test the configuration space when using include and exclude + + Expects + ------- + * Including a classifier choice has pipeline give back matching choice + * Excluding a classifier choice means it won't show up in the hyperparameter space + * Including a feature preprocessor has pipeline give back matching choice + * Excluding a feature preprocessor means it won't show up in the hyperparameter space + """ + # include a classifier choice + pipeline = SimpleClassificationPipeline(include={'classifier': ['libsvm_svc']}) + cs = pipeline.get_hyperparameter_search_space() + + expected = CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']) + returned = cs.get_hyperparameter('classifier:__choice__') + self.assertEqual(returned, expected) - cs = SimpleClassificationPipeline(exclude={'classifier': ['libsvm_svc']}).\ - get_hyperparameter_search_space() + # exclude a classifier choice + pipeline = SimpleClassificationPipeline(exclude={'classifier': ['libsvm_svc']}) + cs = pipeline.get_hyperparameter_search_space() self.assertNotIn('libsvm_svc', str(cs)) - cs = SimpleClassificationPipeline( - include={'feature_preprocessor': ['select_percentile_classification']}).\ - get_hyperparameter_search_space() - fpp1 = cs.get_hyperparameter('feature_preprocessor:__choice__') - fpp2 = CategoricalHyperparameter( - 'feature_preprocessor:__choice__', ['select_percentile_classification']) - self.assertEqual(fpp1, fpp2) + # include a feature preprocessor + pipeline = SimpleClassificationPipeline( + include={'feature_preprocessor': ['select_percentile_classification']} + ) + cs = pipeline.get_hyperparameter_search_space() + + returned = cs.get_hyperparameter('feature_preprocessor:__choice__') + expected = CategoricalHyperparameter( + 'feature_preprocessor:__choice__', + ['select_percentile_classification'] + ) + self.assertEqual(returned, expected) - cs = SimpleClassificationPipeline( + # exclude a feature preprocessor + pipeline = SimpleClassificationPipeline( exclude={'feature_preprocessor': ['select_percentile_classification']} - ).get_hyperparameter_search_space() + ) + cs = pipeline.get_hyperparameter_search_space() self.assertNotIn('select_percentile_classification', str(cs)) def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(self): - cs = SimpleClassificationPipeline( + """Test that the default classifier gets updated based on the legal feature + preprocessors that come before. + + Expects + ------- + * With 'densifier' as only legal feature_preprocessor, 'qda' is default classifier + * With 'nystroem_sampler' as only legal feature_preprocessor, 'sgd' is default classifier + """ + pipeline = SimpleClassificationPipeline( include={'feature_preprocessor': ['densifier']}, - dataset_properties={'sparse': True}).\ - get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter( - 'classifier:__choice__').default_value, - 'qda' + dataset_properties={'sparse': True} ) + cs = pipeline.get_hyperparameter_search_space() + + default_choice = cs.get_hyperparameter('classifier:__choice__').default_value + self.assertEqual(default_choice, 'qda') - cs = SimpleClassificationPipeline( - include={'feature_preprocessor': ['nystroem_sampler']}).\ - get_hyperparameter_search_space() - self.assertEqual(cs.get_hyperparameter( - 'classifier:__choice__').default_value, - 'sgd' + pipeline = SimpleClassificationPipeline( + include={'feature_preprocessor': ['nystroem_sampler']} ) + cs = pipeline.get_hyperparameter_search_space() + + default_choice = cs.get_hyperparameter('classifier:__choice__').default_value + self.assertEqual(default_choice, 'sgd') def test_get_hyperparameter_search_space_only_forbidden_combinations(self): - self.assertRaisesRegex( - AssertionError, - "No valid pipeline found.", - SimpleClassificationPipeline, - include={ - 'classifier': ['multinomial_nb'], - 'feature_preprocessor': ['pca'] - }, - dataset_properties={'sparse': True} - ) + """Test that invalid pipeline configurations raise errors - # It must also be catched that no classifiers which can handle sparse - # data are located behind the densifier - self.assertRaisesRegex( - ValueError, - "Cannot find a legal default configuration.", - SimpleClassificationPipeline, - include={ - 'classifier': ['liblinear_svc'], - 'feature_preprocessor': ['densifier'] - }, - dataset_properties={'sparse': True} - ) + Expects + ------- + * 0 combinations are found with 'multinomial_nb' and 'pca' with 'sparse' data + * Classifiers that can handle sparse but located behind a 'densifier' should + raise that no legal default configuration can be found + """ + with self.assertRaisesRegex(AssertionError, "No valid pipeline found."): + SimpleClassificationPipeline( + include={ + 'classifier': ['multinomial_nb'], + 'feature_preprocessor': ['pca'] + }, + dataset_properties={'sparse': True} + ) + + with self.assertRaisesRegex(ValueError, "Cannot find a legal default configuration."): + SimpleClassificationPipeline( + include={ + 'classifier': ['liblinear_svc'], + 'feature_preprocessor': ['densifier'] + }, + dataset_properties={'sparse': True} + ) @unittest.skip("Wait until ConfigSpace is fixed.") def test_get_hyperparameter_search_space_dataset_properties(self): cs_mc = SimpleClassificationPipeline.get_hyperparameter_search_space( - dataset_properties={'multiclass': True}) + dataset_properties={'multiclass': True} + ) self.assertNotIn('bernoulli_nb', str(cs_mc)) cs_ml = SimpleClassificationPipeline.get_hyperparameter_search_space( @@ -541,51 +723,95 @@ def test_get_hyperparameter_search_space_dataset_properties(self): self.assertEqual(cs_ml, cs_mc_ml) def test_predict_batched(self): + """Test that predict_proba predicts the same as the underlying classifier with + predict_proba argument `batches`. + + Expects + ------- + * Should expect the output shape to match that of the digits dataset + * Should expect a fixed call count each test run + * Should expect predict_proba with `batches` and predict_proba perform near identically + """ cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) # Multiclass X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - cls.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) def test_predict_batched_sparse(self): - cls = SimpleClassificationPipeline(dataset_properties={'sparse': True}, - include={'classifier': ['sgd']}) + """Test that predict_proba predicts the same as the underlying classifier with + predict_proba argument `batches`, with a sparse dataset + + Expects + ------- + * Should expect the output shape to match that of the digits dataset + * Should expect a fixed call count each test run + * Should expect predict_proba with `batches` and predict_proba perform near identically + """ + cls = SimpleClassificationPipeline( + dataset_properties={'sparse': True}, + include={'classifier': ['sgd']} + ) # Multiclass - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) - cls.fit(X_train, Y_train) + X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) def test_predict_proba_batched(self): + """Test that predict_proba predicts the same as the underlying classifier with + predict_proba argument `batches`, for multiclass and multilabel data. + + Expects + ------- + * Should expect the output shape to match that of the digits dataset + * Should expect a fixed call count each test run + * Should expect predict_proba with `batches` and predict_proba perform near identically + """ # Multiclass cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - cls.fit(X_train, Y_train) + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) @@ -595,33 +821,53 @@ def test_predict_proba_batched(self): X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train])) - cls.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + X_test_ = X_test.copy() prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) def test_predict_proba_batched_sparse(self): - + """Test that predict_proba predicts the same as the underlying classifier with + predict_proba argument `batches`, for multiclass and multilabel data. + + Expects + ------- + * Should expect the output shape to match that of the digits dataset + * Should expect a fixed call count each test run + * Should expect predict_proba with `batches` and predict_proba perform near identically + """ cls = SimpleClassificationPipeline( dataset_properties={'sparse': True, 'multiclass': True}, - include={'classifier': ['sgd']}) + include={'classifier': ['sgd']} + ) # Multiclass - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) - cls.fit(X_train, Y_train) + X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) X_test_ = X_test.copy() + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) @@ -629,26 +875,44 @@ def test_predict_proba_batched_sparse(self): # Multilabel cls = SimpleClassificationPipeline( dataset_properties={'sparse': True, 'multilabel': True}, - include={'classifier': ['lda']}) - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) - Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) - for y in Y_train])) - cls.fit(X_train, Y_train) + include={'classifier': ['lda']} + ) + X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', make_sparse=True) + X_test_ = X_test.copy() + Y_train = np.array([[1 if i != y else 0 for i in range(10)] for y in Y_train]) + + with ignore_warnings(classifier_warnings): + cls.fit(X_train, Y_train) + prediction_ = cls.predict_proba(X_test_) + # The object behind the last step in the pipeline cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) cls.steps[-1][-1].predict_proba = cls_predict + prediction = cls.predict_proba(X_test, batch_size=20) + self.assertEqual((1647, 10), prediction.shape) self.assertEqual(84, cls_predict.call_count) np.testing.assert_array_almost_equal(prediction_, prediction) def test_pipeline_clonability(self): + """Test that the pipeline item is clonable with `sklearn.clone` + + Expects + ------- + * The cloned object has all the same param keys + * The cloned object can be constructed from theses params + * The reconstructed clone and the original have the same param values + """ X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') + auto = SimpleClassificationPipeline() - auto = auto.fit(X_train, Y_train) + + with ignore_warnings(classifier_warnings): + auto = auto.fit(X_train, Y_train) + auto_clone = clone(auto) auto_clone_params = auto_clone.get_params() @@ -661,6 +925,7 @@ def test_pipeline_clonability(self): new_object_params = auto.get_params(deep=False) for name, param in new_object_params.items(): new_object_params[name] = clone(param, safe=False) + new_object = klass(**new_object_params) params_set = new_object.get_params(deep=False) @@ -676,30 +941,55 @@ def test_get_params(self): pass def test_add_classifier(self): + """Test that classifiers can be added + + Expects + ------- + * There should be 0 components initially + * There should be 1 component after adding a classifier + * The classifier should be in the search space of the Pipeline after being added + """ self.assertEqual(len(classification_components.additional_components.components), 0) self.assertEqual(len(_addons['classification'].components), 0) + classification_components.add_classifier(DummyClassifier) + self.assertEqual(len(classification_components.additional_components.components), 1) self.assertEqual(len(_addons['classification'].components), 1) + cs = SimpleClassificationPipeline().get_hyperparameter_search_space() self.assertIn('DummyClassifier', str(cs)) + del classification_components.additional_components.components['DummyClassifier'] def test_add_preprocessor(self): + """Test that preprocessors can be added + + Expects + ------- + * There should be 0 components initially + * There should be 1 component after adding a preprocessor + * The preprocessor should be in the search space of the Pipeline after being added + """ self.assertEqual(len(preprocessing_components.additional_components.components), 0) self.assertEqual(len(_addons['feature_preprocessing'].components), 0) + preprocessing_components.add_preprocessor(DummyPreprocessor) + self.assertEqual(len(preprocessing_components.additional_components.components), 1) self.assertEqual(len(_addons['feature_preprocessing'].components), 1) + cs = SimpleClassificationPipeline().get_hyperparameter_search_space() self.assertIn('DummyPreprocessor', str(cs)) + del preprocessing_components.additional_components.components['DummyPreprocessor'] def _test_set_hyperparameter_choice(self, expected_key, implementation, config_dict): - """ - Given a configuration in config, this procedure makes sure that - the given implementation, which should be a Choice component, honors - the type of the object, and any hyperparameter associated to it + """Given a configuration in config, this procedure makes sure that the given + implementation, which should be a Choice component, honors the type of the + object, and any hyperparameter associated to it + + TODO: typing """ keys_checked = [expected_key] implementation_type = config_dict[expected_key] @@ -744,16 +1034,19 @@ def _test_set_hyperparameter_choice(self, expected_key, implementation, config_d else: raise ValueError("New type of pipeline component!") return keys_checked + for key, value in config_dict.items(): if key != expected_key and expected_sub_key in key: expected_attributes[key.split(':')[-1]] = value keys_checked.append(key) + if expected_attributes: attributes = vars(implementation.choice) # Cannot check the whole dictionary, just names, as some # classes map the text hyperparameter directly to a function! for expected_attribute in expected_attributes.keys(): self.assertIn(expected_attribute, attributes.keys()) + return keys_checked def _test_set_hyperparameter_component(self, expected_key, implementation, config_dict): @@ -761,6 +1054,8 @@ def _test_set_hyperparameter_component(self, expected_key, implementation, confi Given a configuration in config, this procedure makes sure that the given implementation, which should be a autosklearn component, honors is created with the desired hyperparameters stated in config_dict + + TODO: typing """ keys_checked = [] attributes = vars(implementation) @@ -796,7 +1091,7 @@ def test_set_hyperparameters_honors_configuration(self): Also considers random_state and ensures pipeline steps correctly recieve the right random_state """ - + random_state = 1 all_combinations = list(itertools.product([True, False], repeat=4)) for sparse, multilabel, signed, multiclass, in all_combinations: dataset_properties = { @@ -805,7 +1100,6 @@ def test_set_hyperparameters_honors_configuration(self): 'multiclass': multiclass, 'signed': signed, } - random_state = 1 cls = SimpleClassificationPipeline( random_state=random_state, dataset_properties=dataset_properties, @@ -864,7 +1158,9 @@ def test_fit_instantiates_component(self): # We reduce the search space as forbidden clauses prevent to instantiate # the user defined preprocessor manually - cls = SimpleClassificationPipeline(include={'classifier': ['random_forest']}) + cls = SimpleClassificationPipeline( + include={'classifier': ['random_forest']} + ) cs = cls.get_hyperparameter_search_space() self.assertIn('CrashPreprocessor', str(cs)) config = cs.sample_configuration() @@ -875,13 +1171,14 @@ def test_fit_instantiates_component(self): # to clean up with check in the future del preprocessing_components.additional_components.components['CrashPreprocessor'] self.fail("cs={} config={} Exception={}".format(cs, config, e)) + cls.set_hyperparameters(config) - with self.assertRaisesRegex( - ValueError, - "Make sure fit is called" - ): - cls.fit( - X=np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), - y=np.array([1, 0, 1, 1]) - ) + + with self.assertRaisesRegex(ValueError, "Make sure fit is called"): + with ignore_warnings(classifier_warnings): + cls.fit( + X=np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), + y=np.array([1, 0, 1, 1]) + ) + del preprocessing_components.additional_components.components['CrashPreprocessor'] diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 03d1e9e321..cc52109664 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -1,9 +1,7 @@ import copy import itertools import resource -import sys import tempfile -import traceback import unittest import unittest.mock @@ -28,6 +26,8 @@ from autosklearn.pipeline.util import get_dataset from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS +from test.test_pipeline.ignored_warnings import regressor_warnings, ignore_warnings + class SimpleRegressionPipelineTest(unittest.TestCase): _multiprocess_can_split_ = True @@ -123,10 +123,10 @@ def test_multioutput(self): 'X_test': X_test, 'Y_test': Y_test} dataset_properties = {'multioutput': True} - cs = SimpleRegressionPipeline(dataset_properties=dataset_properties).\ - get_hyperparameter_search_space() - self._test_configurations(cs, data=data, - dataset_properties=dataset_properties) + pipeline = SimpleRegressionPipeline(dataset_properties=dataset_properties) + cs = pipeline.get_hyperparameter_search_space() + + self._test_configurations(cs, data=data, dataset_properties=dataset_properties) def _test_configurations(self, configurations_space, make_sparse=False, data=None, dataset_properties=None): @@ -180,7 +180,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, check_is_fitted(step) try: - cls.fit(X_train, Y_train) + with ignore_warnings(regressor_warnings): + cls.fit(X_train, Y_train) + # After fit, all components should be tagged as fitted # by sklearn. Check is fitted raises an exception if that # is not the case @@ -212,10 +214,13 @@ def _test_configurations(self, configurations_space, make_sparse=False, elif 'The condensed distance matrix must contain only finite ' \ 'values.' in e.args[0]: continue + elif "zero-size array to reduction operation maximum which has no " \ + "identity" in e.args[0]: + continue else: - print(config) - print(traceback.format_exc()) + e.args += (f"config={config}",) raise e + except RuntimeWarning as e: if "invalid value encountered in sqrt" in e.args[0]: continue @@ -228,22 +233,21 @@ def _test_configurations(self, configurations_space, make_sparse=False, elif "invalid value encountered in multiply" in e.args[0]: continue else: - print(config) - traceback.print_tb(sys.exc_info()[2]) + e.args += (f"config={config}",) raise e + except UserWarning as e: if "FastICA did not converge" in e.args[0]: continue else: - print(config) - traceback.print_tb(sys.exc_info()[2]) + e.args += (f"config={config}",) raise e + except Exception as e: if "Multiple input features cannot have the same target value" in e.args[0]: continue else: - print(config) - traceback.print_tb(sys.exc_info()[2]) + e.args += (f"config={config}",) raise e def test_default_configuration(self): diff --git a/test/test_util/example_config.yaml b/test/test_util/example_config.yaml index 84849a9b5e..4b91cce7a2 100644 --- a/test/test_util/example_config.yaml +++ b/test/test_util/example_config.yaml @@ -34,7 +34,7 @@ loggers: handlers: [file_handler] propagate: no - autosklearn.util.backend: + autosklearn.automl_common.common.utils.backend: level: DEBUG handlers: [file_handler] propagate: no diff --git a/test/test_util/test_backend.py b/test/test_util/test_backend.py index 4a62589358..a029aef4bb 100644 --- a/test/test_util/test_backend.py +++ b/test/test_util/test_backend.py @@ -3,7 +3,7 @@ import unittest import unittest.mock -from autosklearn.util.backend import Backend +from autosklearn.automl_common.common.utils.backend import Backend class BackendModelsTest(unittest.TestCase): @@ -23,7 +23,7 @@ def test_load_model_by_seed_and_id(self, exists_mock, pickleLoadMock): exists_mock.return_value = False open_mock = unittest.mock.mock_open(read_data='Data') with unittest.mock.patch( - 'autosklearn.util.backend.open', + 'autosklearn.automl_common.common.utils.backend.open', open_mock, create=True, ):